Micro???structural tissue analysis for automatic histopathological image annotation (original) (raw)
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Histopathological image classification using stain component features on a pLSA model
Progress in Pattern Recognition, Image Analysis, …, 2010
Semantic annotation of microscopical field of views is one of the key problems in computer assistance of histopathological images. In this paper a new method for extracting patch descriptors is proposed and evaluated using a probabilistic latent semantic analysis (pLSA) classification model. The proposed approach is based on the analysis of the different dyes used to stain the histological sample. This analysis allows to find local regions that correspond to cells in the image, which are then described by the SIFT descriptors of the stain components. The proposed approach outperforms the conventional sampling and description strategies, proposed in the literature.
Histopathological Image Analysis: A Review
IEEE Reviews in Biomedical Engineering, 2009
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.
Image Analysis and Machine Learning Techniques for Digital Histopathological Images
2021
• Chapter 3 of this thesis has been published as {Salah Alheejawi, Hongming Xu, Richard Berendt, Naresh Jha, and Mrinal Mandal, "Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images" Computerized Medical Imaging and Graphics, Volume 73, Pages 19-29, 2019}. I was responsible for the experiment design, implementation, signal processing, data collection and analysis, and manuscript composition. Hongming Xu helped in the implementation process. Dr. Richard Berendt, and Dr. Naresh Jha provided the medical data used in the manuscript, ground truth results and medicine specific expertise. Prof. Mrinal Mandal was the supervisory author and was involved with concept formation and manuscript composition. This chapter proposes an automated technique to segment the lymph node regions on histopathological images obtained with various stains such as H&E, MART-1, S-100, CD-45 and Ki-67. The technique then determines the melanoma regions on a Ki-67-stained image to calculate the proliferation index. A few Regions of Interests with high PI values for grading by the pathologists.
Histopathological Image Analysis Using Image Processing Techniques: An Overview
Signal & Image Processing : An International Journal, 2012
This paper reviews computer assisted histopathology image analysis for cancer detection and classification. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analyzing and classifying most of the diseases like cancer. The analysis of histoapthological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image. This is very time consuming and more prone to intra and inter observer variability. To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis of tissue. In this paper we reviews and summarize the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.
Automated histopathological image analysis: a review on ROI extraction
This review paper deals with the latest technology developed on computer assisted analysis for histopathology images. The process for locating, analyzing and classification of lethal diseases like cancer, using a microscope by the pathologists is termed as histopathology. Since from decade, this is done through a manual process by the pathologists, which is entirely dependent on the level of expertise of the examiner. The analyzations of digital slides are based on the structure of tissue, cell distribution, and cell shape regularities. The whole process is more prone to internal as well as external observer. In this paper, the computerized image analysis process is reviewed for quantitative tissue diagnosis from histopathology images. A summary is presented, for digital image processing techniques, which is applicable to the area of histopathology digital slide analysis. Automatic extraction of discriminative area from histopathology digital slides is a significant research area. This paper describes the current state of art for extraction of discriminative area or region of interest from histology digital slides and various classification methods for analyzing these digital slides.
A Brief Study on Histopathological Images
INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH
The process of examining biological tissue under a microscope for detecting the severity of the disease is called histology, it is an essential technique in biomedical research and clinical practice. While slide preparation and imaging is increasingly becoming automated, but the analysis of histology images still require the trained eyes of a pathologist to examine under a microscope. Processing tissues from histopathological images has become now fully computerized, significantly increasing the speed, the labs can produce tissue slides for viewing images digitally. Digitizing these slides, allows pathologist to view these slides on a computer rather than on microscope. routine analysis of tissues selection will be very difficult, manual task that can be completed only by trained pathologists at a huge cost. In the clinical domain, these methods could improve the accuracy and consistency of diagnoses and hence pathologists can focus on the most difficult cases. This research domain could complete the tasks that are time-consuming for humans, and discover new diseases from millions of whole-slide images (WSIs) or precisely delineating tissues within a tumor, allowing for a quantitative comparison of tumours grown under different conditions.
Automated identification of microstructures on histology slides
2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), 2004
Grading of breast cancer and the subsequent treatment options are largely dependent on the pathological examination of the histology slides from the tumor tissue. Tumor grading is currently based on the spatial organization of the tissue, including the distribution of cancer cells, the morphological properties of their nuclei and the presence/absence of cancerassociated surface receptors these cells express. In this study, we have developed an automated image processing method to detect and identify clinically relevant microscopic structures on histology slides. The tissue components identified with our program are as follows: fat cells, stroma, and three morphologically distinct cell nuclei types used in grading cancer on the Haematoxylin and Eosin (H&E) stained slides. The image processing is based on gray-scale segmentation, feature extraction, supervised learning, subsequent training and clustering. Our automated processing system has an accuracy of 89% ±0.8 in correctly identifying the three different nuclei types observed in H & E stained histology slides.
The pathological image segmentation is important in cancer diagnosis and grading. In human body, tissues are characterized with the organization of their components. Cancer causes the changes in these organization. In order to diagnose the cancer disease, pathologist visually examine the changes in the tissue. This examination mainly relies on the visual interpretation. It may lead to considerable amount of observer variability. Hence, they may or may not identify the abnormal tissue. To avoid this problem robust algorithms are introduced for segmentation. Graph Run Length Method (GRLM), Gray Level Co-occurrence Matrix (GLCM) provides efficient way to segment the abnormal tissue. To a pathological image color graph was automatically generated by using Graph Run Length Method (GRLM). Gray Level Co-occurrence Matrix (GLCM) provides texture features of pathological image. The graph provides the arrangement of cells and structure of cells in a tissue. Based on the arrangement of cells, structure of cells, GLCM based texture features we can segment the abnormal tissue efficiently.
Histological stain evaluation for machine learning applications
Journal of pathology informatics, 2013
Aims: A methodology for quantitative comparison of histological stains based on their classification and clustering performance, which may facilitate the choice of histological stains for automatic pattern and image analysis. Background: Machine learning and image analysis are becoming increasingly important in pathology applications for automatic analysis of histological tissue samples. Pathologists rely on multiple, contrasting stains to analyze tissue samples, but histological stains are developed for visual analysis and are not always ideal for automatic analysis. Materials and Methods: Thirteen different histological stains were used to stain adjacent prostate tissue sections from radical prostatectomies. We evaluate the stains for both supervised and unsupervised classification of stain/tissue combinations. For supervised classification we measure the error rate of nonlinear support vector machines, and for unsupervised classification we use the Rand index and the F-measure to assess the clustering results of a Gaussian mixture model based on expectation-maximization. Finally, we investigate class separability measures based on scatter criteria. Results: A methodology for quantitative evaluation of histological stains in terms of their classification and clustering efficacy that aims at improving segmentation and color decomposition. We demonstrate that for a specific tissue type, certain stains perform consistently better than others according to objective error criteria. Conclusions: The choice of histological stain for automatic analysis must be based on its classification and clustering performance, which are indicators of the performance of automatic segmentation of tissue into morphological components, which in turn may be the basis for diagnosis.