Graph run-length matrices for histopathological image segmentation

Cataloged from PDF version of article.The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentatio

Image Segmentation using Various Approaches

This paper addresses the issue of image segmentation. Image segmentation process is the main basic process or technique used in various image processing problem domains, for example, content based image retrieval; pattern recognition; object recognition; face recognition; medical image processing; fault detection in product industries; etc. Scope of improvement exists in the following areas: Image partitioning; color based feature; texture based feature, searching mechanism for similarity; cluster formation logic; pixel connectivity criterion; intelligent decision making for clustering; processing time; etc. This paper presents the image segmentation mechanism which addresses few of the identified areas where the scope of contribution exists. Presented work basically deals with the development of the mechanism which is divided into three parts first part focuses on the color based image segmentation using k-means clustering methodology. Second part deals with region properties based segmentation. Later, deals with the boundary based segmentation. In all these three approaches, finally the Steiner tree is created to identify the class of the region. For this purpose the Euclidean distance is used. Experimental result justifies the application of the developed mechanism for the image segmentation

Tissue object patterns for segmentation in histopathological images

In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is subject to a considerable amount of observer variability. In order to decrease the variability, it has been proposed to develop systems that mathematically model the histopathological tissue images and automate the analysis. Segmentation constitutes the first step for most of these automated systems. Nevertheless, the segmentation in histopathological images remains a challenging task since these images typically show variances due to their complex nature and may include a large amount of noise and artifacts due to the tissue preparation procedures. In our research group, we recently developed different segmentation algorithms that rely on representing a tissue image with a set of tissue objects and using the structural pattern of these objects in segmentation. In this paper, we review these segmentation algorithms, discussing their clinical demonstrations on colon tissues. © 2011 ACM

Automated analysis of colorectal cancer

Colorectal cancer (CRC) is the second largest cause of cancer deaths in the UK, with approximately 16,000 per year. Over 41,000 people are diagnosed annually, and 43% of those will die within ten years of diagnosis. The treatment of CRC patients relies on pathological examination of the disease to identify visual features that predict growth and spread, and response to chemoradiotherapy. These prognostic features are identified manually, and are subject to inter and intra-scorer variability. This variability stems from the subjectivity in interpreting large images which can have very varied appearances, as well as the time consuming and laborious methodology of visually inspecting cancer cells. The work in this thesis presents a systematic approach to developing a solution to address this problem for one such prognostic indicator, the Tumour:Stroma Ratio (TSR). The steps taken are presented sequentially through the chapters, in order of the work carried out. These specifically involve the acquisition and assessment of a dataset of 2.4 million expert-classified images of CRC, and multiple iterations of algorithm development, to automate the process of generating TSRs for patient cases. The algorithm improvements are made using conclusions from observer studies, conducted on a psychophysics experiment platform developed as part of this work, and further work is undertaken to identify issues of image quality that affect automated solutions. The developed algorithm is then applied to a clinical trial dataset with survival data, meaning that the algorithm is validated against two separate pathologist-scored, clinical trial datasets, as well as being able to test its suitability for generating independent prognostic markers