Purpose

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images.

Design/methodology/approach

The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach.

Findings

The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%.

Originality/value

The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

中文翻译：

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基于复合小波的主动轮廓线医学图像分割

目的

在临床分析中，医学图像分割是研究解剖结构的重要步骤。这有助于诊断和分类图像中的异常。图像形态的广泛变化以及仪器采集过程的局限性使得这种分割具有挑战性。本文旨在提出一种半自动模型来应对这些挑战并分割医学图像。

设计/方法/方法

作者提出了基于勒让德多项式的活动轮廓，以使用软计算和多分辨率框架从嘈杂的，低分辨率和不均匀的医学图像中分割出感兴趣区域（ROI）。在第一阶段，使用模糊C均值（FCM）聚类从低分辨率医学图像中获取初始分割（即先前的聚类），并使用基于小波能量的多分辨率方法抑制噪声。在第二阶段，使用基于勒让德多项式的水平集方法获得分割结果。

发现

在不同的医学图像（例如用于脑肿瘤识别的X射线图像，磁共振成像（MRI），脊柱图像，血细胞和血管）上测试了提出的模型。通过计算对噪声，所需处理时间和分割精度的改进，可以对模型进行严格的分析。对比分析得出的结论是，所提出的模型能够承受噪声并成功地分割了任何类型的医疗模式，平均准确率达到99.57％。

创意/价值

提出的设计是对Legendre水平集（L2S）模型的改进。FCM和小波变换在L2S中的集成使模型对噪声和强度不均匀性不敏感，因此，即使对于L2S无法对其进行分割的图像，它也可以成功地从多种医学图像中分割出ROI。