Colour Model Analysis for Histopathology Image Processing

This chapter presents a comparative study among different colour models (RGB, HSI, CMYK, CIEL*a*b*, and HSD) applied to very large microscopic image analysis. Such analysis of different colour models is needed in order to carry out a successful detection and therefore a classification of different regions of interest (ROIs) within the image. This, in turn, allows both distinguishing possible ROIs and retrieving their proper colour for further ROI analysis. This analysis is not commonly done in many biomedical applications that deal with colour images. Other important aspect is the computational cost of the different processing algorithms according to the colour model. This work takes these aspects into consideration to choose the best colour model tailored to the microscopic stain and tissue type under consideration and to obtain a successful processing of the histological image.

[1]  Steve McLaughlin,et al.  Comparative study of textural analysis techniques to characterise tissue from intravascular ultrasound , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[2]  Freida L. Carson,et al.  Histotechnology: A Self-Instructional Text , 1990 .

[3]  George Lee,et al.  Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data , 2011, Comput. Medical Imaging Graph..

[4]  Isabelle Salmon,et al.  KI-67 hot-spots detection on glioblastoma tissue sections , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[5]  Sarah A. Douglas,et al.  Model and representation: the effect of visual feedback on human performance in a color picker interface , 1999, TOGS.

[6]  Gloria Bueno García,et al.  Critical Comparison of 31 Commercially Available Digital Slide Systems in Pathology , 2006, International journal of surgical pathology.

[7]  Christel Daniel-Le Bozec,et al.  Standardizing the use of whole slide images in digital pathology , 2011, Comput. Medical Imaging Graph..

[8]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[9]  Emily S. Patterson,et al.  Barriers and facilitators to adoption of soft copy interpretation from the user perspective: Lessons learned from filmless radiology for slideless pathology , 2011, Journal of pathology informatics.

[10]  A. Ruifrok,et al.  Comparison of Quantification of Histochemical Staining By Hue-Saturation-Intensity (HSI) Transformation and Color-Deconvolution , 2003, Applied immunohistochemistry & molecular morphology : AIMM.

[11]  Andrew Janowczyk,et al.  A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation , 2010, Medical Imaging.

[12]  Nicolas Loménie,et al.  Time-efficient sparse analysis of histopathological whole slide images , 2011, Comput. Medical Imaging Graph..

[13]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[14]  R.W.G. Hunt,et al.  The Reproduction of Colour: Hunt/Reproduction of Colour , 2005 .

[15]  Metin Nafi Gürcan,et al.  Special issue on whole slide microscopic image processing , 2011, Comput. Medical Imaging Graph..

[16]  John A. Kiernan,et al.  Histological and Histochemical Methods: Theory and Practice , 1981 .

[17]  Anant Madabhushi,et al.  Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[18]  Metin Nafi Gürcan,et al.  Automatic detection of follicular regions in H&E images using iterative shape index , 2011, Comput. Medical Imaging Graph..

[19]  Min Hu,et al.  Automated cell nucleus segmentation using improved snake , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[20]  Robert W. G. Hunt,et al.  The reproduction of colour , 1957 .

[21]  Elizabeth Selvin,et al.  A comprehensive histopathological evaluation of vascular medial fibrosis: insights into the pathophysiology of arterial stiffening. , 2010, Atherosclerosis.

[22]  Yulei Jiang,et al.  Computer-aided identification of prostatic adenocarcinoma: Segmentation of glandular structures , 2011, Journal of pathology informatics.

[23]  Keerthana Prasad,et al.  Automation of immunohistochemical evaluation in breast cancer using image analysis. , 2011, World journal of clinical oncology.

[24]  A. Madabhushi.  Digital pathology image analysis: opportunities and challenges. , 2009, Imaging in medicine.

[25]  G. V. Ommen,et al.  A 3 months mild functional test regime does not affect disease parameters in young mdx mice , 2010, Neuromuscular Disorders.

[26]  Padraig Cunningham,et al.  Ensemble based system for whole-slide prostate cancer probability mapping using color texture features , 2011, Comput. Medical Imaging Graph..

[27]  Abderrahim Elmoataz,et al.  Multi-resolution graph-based analysis of histopathological whole slide images: Application to mitotic cell extraction and visualization , 2011, Comput. Medical Imaging Graph..

[28]  Godwin Avwioro,et al.  HISTOCHEMICAL USES OF HAEMATOXYLIN - A REVIEW , 2011 .

[29]  A R Smith,et al.  Color Gamut Transformation Pairs , 1978 .

[30]  J. A. van der Laak,et al.  Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy. , 2000, Cytometry.

[31]  Ehud Rivlin,et al.  Cell nuclei segmentation using fuzzy logic engine , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[32]  John C. Beatty,et al.  An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models , 1987, TOGS.

[33]  Peter Meer,et al.  Unsupervised segmentation based on robust estimation and color active contour models , 2005, IEEE Transactions on Information Technology in Biomedicine.

[34]  Vilppu J Tuominen,et al.  ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 , 2010, Breast Cancer Research.

[35]  Alvy Ray Smith,et al.  Color gamut transform pairs , 1978, SIGGRAPH.

[36]  Ming Gao,et al.  Computer-aided prostrate cancer diagnosis using image enhancement and JPEG2000 , 2003, SPIE Optics + Photonics.

[37]  Tong Tang,et al.  Proceedings of the European Symposium on Artificial Neural Networks , 2006 .

[38]  Adel Hafiane,et al.  Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection , 2008, ACIVS.

[39]  Tarvinder K Taneja,et al.  Markers of small cell lung cancer , 2004, World journal of surgical oncology.

[40]  Isabelle Salmon,et al.  Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis , 2009, Proteomics.

[41]  Arie E. Kaufman,et al.  A human factors study of color notation systems for computer graphics , 1982, CACM.

[42]  Anant Madabhushi,et al.  A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies , 2012, IEEE Transactions on Biomedical Engineering.

[43]  Dongming Lu,et al.  Interactive color image segmentation by region growing combined with image enhancement based on Bezier model , 2004, Third International Conference on Image and Graphics (ICIG'04).

[44]  G. Svaetichin,et al.  Spectral response curves from single cones. , 1956, Acta physiologica Scandinavica. Supplementum.

[45]  Metin Nafi Gürcan,et al.  Computer-Aided Detection of Centroblasts for Follicular Lymphoma Grading Using Adaptive Likelihood-Based Cell Segmentation , 2010, IEEE Transactions on Biomedical Engineering.

[46]  Oscar Déniz,et al.  A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering , 2011, Journal of pathology informatics.

[47]  Ignacio Rojas,et al.  Automatic classification of prostate cancer using pseudo-gaussian radial basis function neural network , 2005, ESANN.

[48]  Po-Whei Huang,et al.  Automatic Classification for Pathological Prostate Images Based on Fractal Analysis , 2009, IEEE Transactions on Medical Imaging.

[49]  Enrico Macii,et al.  Automated segmentation of tissue images for computerized IHC analysis , 2022 .

[50]  Llewellyn Bd,et al.  Nuclear staining with alum hematoxylin. , 2009 .

[51]  Mikhail Teverovskiy,et al.  Automated prostate cancer diagnosis and Gleason grading of tissue microarrays , 2005, SPIE Medical Imaging.

[52]  Enrico Macii,et al.  Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: A quantitative evaluation , 2010, Comput. Medical Imaging Graph..