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Image Processing: A Novel Computer Aided Molecular Modeling approach for Analysis of Cancer using MATLAB
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 181 - Number 19 |
| Year of Publication: 2018 |
| Authors: Susmila Aparna Gaddam, Venkata S. Kotakadi, L. V. Narasimha Prasad |
10.5120/ijca2018917883
|
Susmila Aparna Gaddam, Venkata S. Kotakadi, L. V. Narasimha Prasad . Image Processing: A Novel Computer Aided Molecular Modeling approach for Analysis of Cancer using MATLAB. International Journal of Computer Applications. 181, 19 ( Sep 2018), 24-28. DOI=10.5120/ijca2018917883
Abstract
Emphysema is a common chronic respiratory disorder characterised by the destruction of lung tissue. A standard approach used for emphysema detection in medical image analysis and other computerized techniques used for classification of emphysema. The Cancer detection and classification of histopathological images is the standard clinical practice for the diagnosis and prognosis of any cancer. In this paper we present the cancer detection and classification of benign and malignant tumour (Nuclei) based on H & E stained histopathology and colour segmentation based staining method to distinguish the different types of tissues in biomedical application. Nucleus detection in H&E is a challenging problem considering the variability, heterogeneity, low contrast, K means clustering, and differing typologies of nuclei to distinguish different types of tissues. There are strong indications that morphological analysis in H&E can serve as a biomarker. This paper is about developing a image processing method for H&E stained cancer images and these method which helps pathologists to distinguish different types of tissues in biomedical applications. The segmentation approach is completely colour based and uses k-means clustering technique uses a series of algorithm steps which is an image processing approach in distinguishing the different tissue types. These algorithm steps are modelled in image processing tool box of MATLAB v7.0. Modelling steps involved are from reading the image to segmentation of the nuclei into a separate image in MATLAB real-time simulation environment.
References
- A. G. N. Agustí, B. G. Cosío, “Chronic Obstructive Pulmonary Disease Emphysema”, general Encyclopedia of Respiratory Medicine, 2006, Pages 448-457
- Friman O, Borga M, Lundberg M, Tyle´n U, Knutsson H (2002) Recognizing emphysema—a neural network approach. In: Proceedings of sixteenth international conference on pattern recognition, Quebec, Canada, pp 1–4
- Kinsella M, Mu¨ller NL, Abboud RT, Morrison NJ, DyBuncio A (1990) Quantification of emphysema by computed tomography using a ‘‘density mask’’ program and correlation with pulmonary function tests. Chest 97:315–321
- Mir H, Hanmandlu M, Tandon SN (1995) Texture analysis of CT images. IEEE Eng Med Biol Mag 14:781–786
- K.J.R. Murthy, J.G. Sastry “Economic burden of chronic obstructive pulmonary disease”, NCMH Background Papers• Burden of Disease in India
- Sanders C, Nath PH, Bailey WC. “ Detection of emphysema with computed tomography: correlation with pulmonary function tests and chest radiography”. Invest Radiol 1988;23:262-266.
- Pierre Loubeyre, Micheline Paret, Didier Revel, Thérèse Wie77sendanger, and Jean Brune, “Thin-Section CT Detection of Emphysema Associated With Bronchiectasis and Correlation With Pulmonary Function Tests”, CHEST February 1996 vol. 109 no. 2 360-365
- PJohn D. Newell, Jr MD, “CT of emphysema, Radiologic Clinics of North America” Volume 40, Issue 1, January 2002, Pages 31-42.
- Scarff RW, Torloni H: Histological typing of breast tumors. International histological classification of tumors. World Health Organization 1968, 2(2):13-20.
- Balslev I, Axelsson CK, Zedeler K, Rasmussen BB, Carstensen B, Mouridsen HT: The Nottingham Prognostic Index applied to 9,149 patients from the studies of the Danish Breast Cancer Cooperative Group (DBCG). Breast Cancer Res Treat 1994, 32:281-290.
- Latson L, Sebek B, Powell KA: Automated cell nuclear segmentation in color images of hematoxylin and eosin-stained breast biopsy. Analytical and Quantitative Cytology and Histology 2003, 25(6):321-331.
- Schnorrenberg F, Pattichis C, Kyriacou K, Schizas C: Computer- Aided Detection of Breast Cancer Nuclei. IEEE Trans on Information Tech in Biomedicine 1997, 1(2):128-140.
- Schupp S, Elmoataz A, Herlin P, Bloyet D: Mathematical morphologic segmentation deticated to quantitative immunohistochemistry. Analytical and Quantitative Cytology and Histology 2001, 23(4):257-267.
- Venkata Subbaiah Kotakadi, Susmila Aparna Gaddam and D.V.R. Sai Gopal, 2012. “Computer Aided Molecular Modeling approach of H & E ( Haemotoxylin & Eosin) images of Colon Cancer” International Journal of Computer Applications, Volume 44– No.9, April 2012 page 5-8 (ISSN:0975 – 8887)
- Bacus JW, Boone Charles W, Bacus James V, Michele Follen, Kelloff Gary J, Valery Kagan, Scott Lippman M: Image Morphometric Nuclear Grading of Intraepithelial Neoplastic Lesions with Applications to Cancer Chemoprevention Trials. Cancer Epidemiol Biomarkers Prev 1999, 8:1087-1094.
- Hoque A, Lippman Scott M, Boiko Iouri V, Atkinson Edward N, Nour Sneige, Aysegul Sahin, Weber Diane M, Seymon Risin, Lagios Michael D, Roland Schwarting, Colburn William J, Kapil Dhingra, Michele Follen, Kelloff Gary J, Boone Charles W, Hittelman Walter N: Quantitative Nuclear Morphometry by Image Analysis for Prediction of Recurrence of Ductal Carcinoma in Situ of the Breast. Cancer Epidemiol Biomarkers Prev 2001, 10:249-259.
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