Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X‐ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta‐Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.

中文翻译：

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用于生物医学分析的有限倒Beta-Liouville混合模型的在线变分学习

图像分割被广泛应用于生物医学图像分析。但是，由于许多图像模式，例如CT，X射线，MRI，显微镜等，医学图像的分割具有挑战性。另一个挑战是生物医学图像背景中的高可变性，边缘缺失的不一致区域，缺少纹理对比以及高噪声。因此，已经研究了许多分割方法来解决这些问题并将医学图像转换成有意义的信息。在过去的十年中，有限混合模型已被证明是数据聚类中最灵活，最受欢迎的方法之一。在本文中，我们提出了用于对医学图像进行聚类的有限倒置Beta-Liouville混合模型在线变分学习的统计框架。在线变分学习框架用于同时估计参数和混合组分的数量，从而降低了模型的计算复杂性。为此，我们在五个不同的生物医学图像数据集（包括糖尿病性视网膜病变的视盘检测和定位，黑素瘤病变检测和分割的数字成像，脑肿瘤检测，结肠癌检测和计算机辅助检测（CAD））上评估了我们提出的算法疟疾。此外，我们将提出的算法与其他三种流行算法进行了比较。在我们的结果中，我们分析了所提出的有限IBL混合模型算法在线变分学习在医学图像的多种模态上可以准确执行。它以高置信度检测疾病模式。像本文介绍的那样，计算和统计方法对医学图像的分析和解释在临床应用和科学研究中都具有重大影响。我们认为，所提出的算法具有处理多模式生物医学图像数据集的能力，并且可以被研究人员进一步用于分析正确的疾病模式。