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SegCaps: An efficient SegCaps network‐based skin lesion segmentation in dermoscopic images Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-25 Gouse Mohiuddin Kosgiker; Anupama Deshpande; Anjum Kauser
This research aims to improve the efficiency of skin lesion segment locations for the given input image of skin cancer using a combination of recently modified segmentation algorithms. Skin lesion segmentation is still a challenging task in medical image analysis because of the low contrast and high noise produced by dermoscopic imaging. Previous works extracted spatially‐oriented information but failed
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Glioma grade detection using grasshopper optimization algorithm‐optimized machine learning methods: The Cancer Imaging Archive study Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-21 Mohammadreza Hedyehzadeh; Keivan Maghooli; Mohammad MomenGharibvand
Detection of brain tumor's grade is a very important task in treatment plan design which was done using invasive methods such as pathological examination. This examination needs resection procedure and resulted in pain, hemorrhage and infection. The aim of this study is to provide an automated non‐invasive method for estimation of brain tumor’s grade using Magnetic Resonance Images (MRI). After pre‐processing
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Iris boundary localization based on Hough transform and the quadratic circle data compensation Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-21 Yu Yang; Jinsong Wang; Yadi Xue
Iris localization is the crucial link of iris recognition and automatic eye tracking. Based on the traditional Hough transform, this paper proposes an accurate pupil detection method combined with ellipse fitting and circular data compensation. We used the minimum gray mean method to approximately determine the inner edge. According to the results, the inner edge image is extracted and finely located
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Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-20 S. Deepak; P. M. Ameer
The application of deep transfer learning techniques has been successful in developing accurate systems for brain tumour classification on large‐scale medical image databases. For small databases, feature learning by deep neural networks is not robust. The systems based on domain‐specific hand‐crafted features have limited accuracy. In this paper, the authors focus on developing accurate models that
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Anatomical multiatlas segmentation using local texture statistical properties for matching descriptor with machine learning Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-13 Ali Ould Kradda; Abdelghani Ghomari; Abdennacer Ben Hmed; Stephane Binczak
The use of anatomical multiatlas methods has proven to be one of the most competitive techniques for brain images segmentation. The majority of these methods are based on visual criteria of similarity between groups of an atlas to select a representative patient image to be segmented. However, this criterion is not necessarily linked to the performance of the segmentation. To overcome this dilemma
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Measuring panoramic radiomorphometric indices for mandible bone using active shape model and Bayesian information criterion‐support vector machine Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-05 Mehravar Rafati; Fateme Farnia; Elham Romoozi; Ali Mohammad Nickfarjam; Farahnaz Hosseini
This article proposes an automatic method based on a combination of active shape model (ASM) and nonlinear support vector machine (SVM) accompanied with Bayesian information criterion which is utilized in order to measure radiomorphometric indices for digital X‐ray panoramic system. After omitting Poisson noise of input images, the image is divided. It attempts to choose suitable region of interest
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A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-05 Tehmina Khalil; Muhammad Usman Akram; Samina Khalid; Saadat Hanif Dar; Nouman Ali
Glaucoma ocular disease is the second topmost reason for irreversible visual impairment around the world. This malady can be cured and permanent blindness can be prevented by timely diagnosis and treatment. This study is an attempt to analyse the current status of automated glaucoma diagnosis systems. Existing systems have been analysed on the base of ophthalmic imaging technology, capability to detect
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Early detection of breast malignancy using wavelet features and optimized classifier Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2021-01-02 Jayesh George Melekoodappattu; Anoop Balakrishnan Kadan; V Anoop
Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer‐aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based
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Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine‐learning paradigm Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-30 Satya Prakash Sahu; Narendra D. Londhe; Shrish Verma; Bikesh K. Singh; Sumit Kumar Banchhor
Lung cancer is one of the most deadly cancer in both men and women. Accurate and early diagnosis of pulmonary lung nodules is critical. This study presents an accurate computer‐aided diagnosis (CADx) system for risk stratification of pulmonary nodules in computed tomography (CT) lung images by fusing shape and texture‐based features in a machine‐learning (ML) based paradigm. A database with 114 (28
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Differentiation between COVID‐19 and bacterial pneumonia using radiomics of chest computed tomography and clinical features Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-29 Junbang Feng; Yi Guo; Shike Wang; Feng Shi; Ying Wei; Yichu He; Ping Zeng; Jun Liu; Wenjing Wang; Liping Lin; Qingning Yang; Chuanming Li; Xinghua Liu
To develop and validate an effective model for distinguishing COVID‐19 from bacterial pneumonia. In the training group and internal validation group, all patients were randomly divided into a training group (n = 245) and a validation group (n = 105). The whole lung lesion on chest computed tomography (CT) was drawn as the region of interest (ROI) for each patient. Both feature selection and model construction
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Local binary pattern encoding schemes for computed tomography image segmentation: An experimental and comparative study Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-28 Hsien‐Jen Lo; Chih‐Hung Wu
Local binary patterns (LBPs) are used for effective texture representation in various applications. This study explores the clustering consistency and stability of image segmentation when distance‐based clustering methods are used with an LBP. Because data are described by features and attributes, distances among data are also dominated by the definition of features. Moreover, four popular LBP encoding
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Cancer detection using convolutional neural network optimized by multistrategy artificial electric field algorithm Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-19 P. Sinthia; M. Malathi
Recently, image processing schemes are widely used to improve disease detection performance in many medicinal fields. Cancer is considered as one of the most deadly disease and early diagnosis of cancer is the complicated task in the field of medicine. In this paper, we present the two pretrained convolutional neural network (CNN) based on ensemble models such as VGG19 and VGG16 for cancer diagnosis
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Mining frequent approximate patterns in large networks Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-19 Kaouthar Driss; Wadii Boulila; Aurélie Leborgne; Pierre Gançarski
Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so
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Detection and diagnosis of brain tumors using deep learning convolutional neural networks Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-16 Akila Gurunathan; Batri Krishnan
The detection of brain tumors in brain magnetic resonance imaging (MRI) image is an important process for preventing earlier death. This article proposes an automated computer aided method for detecting and locating the brain tumors in brain MRI images using deep learning algorithms. The proposed method has three sub modules as preprocessing, classifications and segmentation. In this article, data
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Sparse structure deep network embedding for transforming brain functional network in early mild cognitive impairment classification Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-14 Zhuqing Jiao; Tingxuan Jiao; Jiahao Zhang; Haifeng Shi; Bona Wu; Yu‐Dong Zhang
Currently, it remains one of the most challenging issues to distinguish brain functional networks of early mild cognitive impairment subjects (eMCIs) and normal control subjects (NCs). Unlike images, functional networks are non‐Euclidean data and not easily classified by dilated convolutional neural network (DCNN). To address this problem, we developed a sparse structure deep network embedding (SSDNE)
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Optimized segmentation and classification for liver tumor segmentation and classification using opposition‐based spotted hyena optimization Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-04 Munipraveena Rela; Suryakari Nagaraja Rao; Patil Ramana Reddy
In today's world, liver cancers are one of the mainly popular cancers occurring in the human body. The greater part of liver carcinomas is more prone to alcohol‐related hepatitis and cirrhosis conditions. Moreover, there is another form of cancer namely, metastatic liver cancer, where the tumor is initiated from other organs and extends to the liver. Early and premature diagnosis of liver cancer is
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Classification of neovascularization on retinal images using extreme learning machine Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-03 Geetha Pavani Pappu; Birendra Biswal; Tapan K. Gandhi; Metta Venkata Satya Sai Ram
Proliferative diabetic retinopathy is the advanced stage of diabetic retinopathy (DR) resulting in the growth of abnormal vessels on the retinal surface termed as neovascularization. This article primarily deals with the timely detection and classification of retinal images into healthy, neovascularization on optic disc, and elsewhere using an extreme learning machine (ELM) classifier. Initially, a
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Automated detection and classification of skin diseases using diverse features and improved gray wolf‐based multiple‐layer perceptron neural network Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-02 K. Melbin; Y. Jacob Vetha Raj
One of the largest organs of the human body is the skin and its pigmentation differs among the population. During skin disease identification, the dermatologist requires a high level of expertise and accuracy. This study proposes different kinds of skin image feature extraction and classification methods. In this work, we have chosen six kinds of skin diseases such as melanoma, seborrheic keratosis
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Lung segmentation on chest X‐ray images in patients with severe abnormal findings using deep learning Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-01 Mizuho Nishio; Koji Fujimoto; Kaori Togashi
Several studies have evaluated the usefulness of deep learning for lung segmentation using chest X‐ray (CXR) images with small‐ or medium‐sized abnormal findings. Here, we built a database including both CXR images with severe abnormalities and experts' lung segmentation results, and aimed to evaluate our network's efficacy in lung segmentation from these images. For lung segmentation, CXR images from
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Compressive sensing theory and neighborhood spatial‐temporal information for frame rate improvement of dynamic ultrasonic imaging Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-12-01 Mina Hosseinpour; Hamid Behnam; Maryam Shojaeifard
The frame rate improvement is an essential issue in dynamic ultrasonic imaging for better displaying rapid heart movements. In this study, a new technique using the compressive sensing (CS) theory was introduced for the frame rate improvement of two‐dimensional (2D) and three‐dimensional (3D) dynamic ultrasonic imaging. In the suggested procedure, a fewer radio frequency (RF) lines were received. Subsequently
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Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-24 Tongxue Zhou; Stéphane Canu; Su Ruan
The coronavirus disease (COVID‐19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID‐19. It is of great importance to rapidly and accurately segment COVID‐19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U‐Net based segmentation network using attention mechanism. As not all the
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An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-23 Deepika Selvaraj; Arunachalam Venkatesan; Vijayalakshmi G. V. Mahesh; Alex Noel Joseph Raj
The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique
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Anatomical‐functional image fusion based on deep convolution neural networks in local Laplacian pyramid domain Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-20 Yuping Huang; Weisheng Li; Jiao Du
Medical image fusion technology makes clinical diagnosis and treatment more accurate. This technique can solve the existing problem that single mode medical images conveying insufficient information. Therefore, the key to this technology is to retain as much as possible information in the original medical image of multiple modes. However, the existing methods often lose source image detail with a low
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New antiscatter grid design by optimization of strip thickness and height Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-20 Abel Zhou; Qi Tan; Graeme L. White; Rob Davidson
Antiscatter grids are used in biomedical X‐ray imaging to improve image quality by reducing scatter radiation reaching the image receptor. However, this comes at the cost of increasing radiation exposure. Grid performance can be improved by optimizing strip‐thickness, which reduces radiation exposure, leading to greater benefits achieved by the grid. Evidence has shown that strip height may also affect
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Dementia MRI image classification using transformation technique based on elephant herding optimization with Randomized Adam method for updating the hyper‐parameters Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-19 N Bharanidharan; Harikumar Rajaguru
The primary objective of this research work is to build a binary classifier for categorizing the input brain magnetic resonanceimaging (MRI) images as either demented or nondemented with high accuracy. A novel hyper‐parameter updating method called Randomized Adam (RanAdam) is proposed for enhancing the dementia classification accuracy of elephant herding optimization algorithm and other swarm intelligence
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Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-18 Sneha Singh; Deep Gupta
Medical image fusion focuses to fuse complementary diagnostic details for better visualization of comprehensive information and interpretation of various diseases and its treatment planning. In this paper, a multistage multimodal fusion model is presented based on nonsubsampled shearlet transform (NSST), stationary wavelet transform (SWT), and feature‐adaptive pulse coupled neural network. Firstly
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Brain micro‐vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high‐resolution phase‐contrast X‐ray tomography Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-15 Alessandra Patera; Antonio G. Zippo; Anne Bonnin; Marco Stampanoni; Gabriele E. M. Biella
High‐throughput synchrotron‐based tomographic microscopy at third generation light sources allows to probe cm‐sized samples at micrometer‐resolution. In this work, we present an approach to image a full mouse brain. With Indian‐ink as a contrast agent, it was possible to obtain 3D distribution of microvessels while a computational framework automatically extracted the morphological and geometrical
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Optimal deep belief network with opposition‐based hybrid grasshopper and honeybee optimization algorithm for lung cancer classification: A DBNGHHB approach Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-15 Lokanath Reddy Chilakala; Gattim Naveen Kishore
In this manuscript, a new approach using deep belief network (DBN) along opposition‐based hybrid grasshopper and honey bee optimization algorithm for lung cancer classification is proposed. Chest computed tomography (CT) is commonly used to diagnosis the lung tumors. Initially, the image quality is improved by preprocessing techniques, and then the features like texture, color and shape are extracted
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Deeply supervised U‐Net for mass segmentation in digital mammograms Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-06 N Ravitha Rajalakshmi; R Vidhyapriya; N Elango; Nikhil Ramesh
Mass detection is a critical process in the examination of mammograms. The shape and texture of the mass are key parameters used in the diagnosis of breast cancer. To recover the shape of the mass, semantic segmentation is found to be more useful rather than mere object detection (or) localization. The main challenges involved in the mass segmentation include: (a) low signal to noise ratio (b) indiscernible
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Particle swarm optimization‐based liver disorder ultrasound image classification using multi‐level and multi‐domain features Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-05 Raghesh Krishnan Krishnamurthy; Sudhakar Radhakrishnan; Mohaideen Abdul Kadhar Kattuva
Liver ultrasound is a cost‐effective, non‐invasive, and sufficient technique to diagnose most of the liver disorders. The recent advancements in research in image processing have led to the development of image‐based liver disorder classification systems. In spite of being popular in the diagnostic imaging of liver, ultrasound images, owing to their poor quality, render the conventional and state of
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Pulmonary lesion classification from endobronchial ultrasonography images using adaptive weighted‐sum of the upper and lower triangular gray‐level co‐occurrence matrix Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-11-01 Banphatree Khomkham; Rajalida Lipikorn
Visual classification of pulmonary lesions from endobronchial ultrasonography (EBUS) images is performed by radiologists; therefore, results can be subjective. Here, two robust features, called the adaptive weighted‐sum of the upper triangular gray‐level co‐occurrence matrix (GLCM) and the adaptive weighted‐sum of the lower triangular GLCM (AWSL), were combined with 22 other standard features and used
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Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-28 Soraya Zehani; Abdeldjalil Ouahabi; Mourad Oussalah; Malika Mimi; Abdelmalik Taleb‐Ahmed
This paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension
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Early detection of melanoma images using gray level co‐occurrence matrix features and machine learning techniques for effective clinical diagnosis Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-27 B. Thiyaneswaran; K. Anguraj; S. Kumarganesh; K. Thangaraj
Melanoma is an early stage of skin cancer. The objective of the proposed work is to detect the symptoms of melanoma early through images of the moles obtained from image processing device and classify the types. The procedure involves converting raw melanoma skin image initially into hue, saturation, and intensity for digital processing. The required information for detecting melanoma is available
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Effect of electrode configuration on recognizing uterine contraction with electrohysterogram: Analysis using a convolutional neural network Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-27 Dongmei Hao; Xiaoxiao Song; Qian Qiu; Xin Xin; Lin Yang; Xiaohong Liu; Hongqing Jiang; Dingchang Zheng
This paper aimed to evaluate the effect of various electrode configurations on applying a convolutional neural network (CNN) to recognize uterine contraction (UC) with Electrohysterogram (EHG) signals. Seven 8‐electrode configurations and thirteen 4‐electrode configurations were selected from the 4 × 4 electrode grid in the Icelandic 16‐electrode EHG database. EHG signals were divided into UC and non‐UC
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Multiple sclerosis identification in brain MRI images using wavelet convolutional neural networks Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-24 Ali Alijamaat; Alireza NikravanShalmani; Peyman Bayat
Multiple sclerosis (MS) is a degenerative disease of the covering around the nerves in the central nervous system. It damages the immune cells and causes small lesions in the patient's brain. Automated image recognition techniques can be employed for increasing the accuracy of detection. The use of convolutional neural networks (CNN) is the most common deep learning method for detecting lesions in
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Triple novelty block detection and classification approach for lung tumor analysis Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-23 K. Vijila Rani; C. Thinkal Dayana; P. Sujatha Therese; M. Eugine Prince
Recently the increased utilization of computer‐aided detection is more helpful to assist the radiologist, recognize the minute lung lesion. In this research article, we propose three novel block detection methods for lung lesion segmentation. First, an automatic algorithm for lung lesion detection and segmentation by using histogram‐based affine‐invariant detection (HAID) is proposed. HAID is a novel
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Aggregated residual transformation network for multistage classification in diabetic retinopathy Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-23 Nitigya Sambyal; Poonam Saini; Rupali Syal; Varun Gupta
Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation‐based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and
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Deep chest X‐ray: Detection and classification of lesions based on deep convolutional neural networks Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-21 Yongwon Cho; Sang Min Lee; Young‐Hoon Cho; June‐Goo Lee; Beomhee Park; Gaeun Lee; Namkug Kim; Joon Beom Seo
We investigated whether a convolutional neural network (CNN) can enhance the usability of computer‐aided detection (CAD) of chest radiographs for various pulmonary abnormal lesions. The numbers of normal and abnormal patients were 6055 and 3463, respectively. Two radiologists delineated regions of interest for lesions and labeled the disease types as ground truths. The datasets were split into training
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Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution neural network architecture Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-21 Charu Bhardwaj; Shruti Jain; Meenakshi Sood
Diabetic retinopathy (DR) accounts in eye‐related disorders due to accumulated damage to small retinal blood vessels. Automated diagnostic systems are effective in early detection and diagnosis of severe eye complications by assisting the ophthalmologists. Deep learning‐based techniques have emerged as an advancement over conventional techniques based on hand‐crafted features. The authors have proposed
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Nonparametric variational learning of multivariate beta mixture models in medical applications Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-20 Narges Manouchehri; Nizar Bouguila; Wentao Fan
Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach
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Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-17 A Anilet Bala; P Aruna Priya; Vivek Maik
Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state‐of‐the‐art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation
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Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-17 Linda Ait Mohamed; Assia Cherfa; Yazid Cherfa; Noureddine Belkhamsa; Fatiha Alim‐Ferhat
Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions
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3D conditional generative adversarial network‐based synthetic medical image augmentation for lung nodule detection Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-15 Tian Bu; Zhiyong Yang; Shan Jiang; Guobin Zhang; Hongyun Zhang; Lin Wei
A computer‐aided detection (CADe) scheme, relying on a large number of high‐quality images with annotations, could help radiologists effectively detect lung nodules. However, such medical data are generally difficult to obtain. To address this issue, this paper proposes a novel method based on a conditional generative adversarial network (CGAN) to generate new samples for data augmentation (DA). This
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Nonsubsampled contourlet transform with cross‐guided bilateral filter for despeckling of medical ultrasound images Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-12 Thapasimuthan Joel; Rajagopal Sivakumar
This paper aims to enhance the image quality in ultrasound images. The significant difficulties in the ultrasound image are the presence of speckle noise. Speckle is the granular noise, and this kind of noise produces a lot of challenges during medical diagnosis. Also, these kinds of problems degrade image quality. The proposed work overcomes this kind of issue with nonsubsampled contourlet domain‐based
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Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-11 Songcheng Li; Junyong Lu; Long Cheng; Xiangping Li
In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the
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2D+t track detection via relative persistent homology Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-09 Rabih Assaf; Alban Goupil; Abbas Rammal; Valeriu Vrabie; Mohammad Kacim
In this paper, we demonstrate that algebraic topology can be used to perform 2D+t object detection. After the construction of a topological complex for a 2D+t image sequence, we build a nested sequence of cell complexes on which relative persistent homology is computed. The relative homology adds to “absolute” homology the computation of classes related to the first and last frames of the sequence
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A novel improved crow‐search algorithm to classify the severity in digital mammograms Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-07 S R Sannasi Chakravarthy; Harikumar Rajaguru
The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either
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A vessel segmentation technique for retinal images Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-06 Mehwish Iqbal; Muhammad Mohsin Riaz; Abdul Ghafoor; Attiq Ahmad
Segmentation of the human eye retinal image is an essential step for proper examination and diagnosis by the ophthalmologists or eye care specialists. A technique for vessel segmentation of retinal images is proposed. Retinal images are mostly low‐light images, which are first processed for enhancement of light as well as for detail amplification. Illumination of low‐light images is enhanced, and details
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Detection of pneumonia in chest X‐ray images by using 2D discrete wavelet feature extraction with random forest Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-05 Abdurrahim Akgundogdu
Pneumonia is one of the most common and fatal diseases in the world. Early diagnosis and treatment are important factors in reducing mortality caused by the aforementioned disease. One of the most important and common techniques to diagnose pneumonia disease is the X‐ray images. By evaluating these images, various machine‐learning methods are used for accuracy in diagnosis. The presented study in this
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Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-10-03 Anthony Muthu Arul Edwin Raj; Muniasamy Sundaram; Thirassama Jaya
The early detection of tumor is necessary to save a number of lives. In women, the temperature of the affected area of the tumor is warmer than the unaffected area; therefore the thermography technique can be used to capture the cancerous breast images with a thermal infrared by identifying the temperature difference between them. Color enhancement of the captured breast image is an important consideration
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Novel local information kernelized fuzzy C‐means algorithm for image segmentation Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-30 Songcheng Li; Junyong Lu; Long Cheng; Xiangping Li
In MRI, the image with poor quality, especially the image with noise interference or low contrast, may provide insufficient data for the visual interpretation of the affected part. Image segmentation provides an effective method to facilitate early detection and further diagnosis. By introducing a Particle Swarm Optimization (PSO) initialization step and a novel dissimilarity measure metric, we present
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Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-28 Bhakti Kaushal; Mukesh D. Patil; Gajanan K. Birajdar
The brain tumor detection is a highly complicated but significant task. The early detection of a brain tumor can increase the survival rate of an individual by providing proper treatment. This work proposes a computer‐aided diagnostic method for brain tumor detection using fractional wavelet transform (FrDWT) with different values of alpha (α) ranging from (0.1‐1), histogram‐based various local feature
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A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-25 Balakumaresan Ragupathy; Manivannan Karunakaran
In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented
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Brain tumor diagnosis based on metaheuristics and deep learning Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-23 An Hu; Navid Razmjooy
The high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues is very time‐consuming and operator dependent. Furthermore, there is a need for experts who can review the images to detect these effects, rendering traditional methods inefficient in their
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Hybrid compression of biomedical ECG and EEG signals based on differential clustering and encoding techniques Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-23 Angeline M; Suja Priyadharsini S
Signal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite
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Glaucoma assessment from color fundus images using convolutional neural network Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-19 Poonguzhali Elangovan; Malaya Kumar Nath
Early detection and proper screening are essential to prevent vision loss due to glaucoma. In recent years, convolutional neural network (CNN) has been successfully applied to the color fundus images for the automatic detection of glaucoma. Compared to the existing automatic screening methods, CNNs have the ability to extract the distinct features directly from the fundus images. In this paper, a deep
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Digital breast tomosynthesis improves diagnostic accuracy of breast microcalcifications Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-18 Weimin Liu; Meijun Long; Lingrong Peng; Caihong Qu; Ruomi Guo; Zhuang Kang; Jin Wang; Juekun Wu; Xiaohong Wang
DBT reconstructs high‐resolution tomographic images through multi‐angle scanning. We aimed at investigating the diagnostic value of DBT in breast microcalcifications of Asian women. The clinical characteristics and diagnostic accuracy of FFDM and DBT in 70 breast cancer patients were compared. 52 malignant lesions and 24 benign lesions were found in 76 breast microcalcifications. FFDM presented with
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Multi‐feature fusion of deep networks for mitosis segmentation in histological images Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-18 Yuan Zhang; Jin Chen; Xianzhu Pan
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Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-14 Seyed Mohammad Alizadeh; Ali Mahloojifar
Melanoma is one of the most dangerous types of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this study, a system is proposed to detect melanoma automatically using an ensemble approach, including convolutional neural networks (CNNs) and image texture feature extraction. Two CNN models, a proposed
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Implementation of convolutional neural network categorizers in coronary ischemia detection Int. J. Imaging Syst. Technol. (IF 1.925) Pub Date : 2020-09-13 Wei Xiao; Qian Gao; Rahul Kumar; C. L. Edwin Yu; Y. E. Janice Ho; Fatima Rashid Sheykhahmad
The heart is one of the most important and sophisticated organ of the human body. Coronary ischemia is a condition in which the coronary muscles do not receive sufficient blood and oxygen because of blocked or tightened heart vessels. This syndrome is called cardiac vessel illness. There have been numerous attempts to detect the impact of cardiac vessel illness on the heart muscles using noninvasive