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Morphological and texture features for cancers tissues microscopic images

Lookup NU author(s): Khaled Marghani, Emeritus Professor Satnam Dlay, Professor Bayan Sharif, Dr Andrew SimsORCiD


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Accurate and reliable decision making in cancer prognosis can help in the planning of appropriate surgery and therapy and, in general, optimize patient management through the different stages of the disease. In this paper, we present a novel fractal geometry algorithm as a potential method for classifying colorectal histopathological images. 102 microscopic samples of colon tissue were examined in order to identify abnormalities using a morphogical feature approach based on segmenting the image into different classes, derived from fractal dimension. The obtained mean fractal dimension (FD) for normal object tissue was 1.797+/-0.0381 (n = 44) compared with 1.866+/-0.0262 for malignant samples (n = 58). In brief, this study was able to demonstrate the value of fractal dimension based on morphological approach in the analysis of microscopic colon cancer images. Although, the obtained results are strongly significant in the separation between normal and malignant colorectal images, further analyses are essential to incorporate this methodology into routine clinical practice by supporting pathologist decision.

Publication metadata

Author(s): Marghani KA, Dlay SS, Sharif BS, Sims A

Editor(s): Sonka M., Fitzpatrick J.M.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Proceedings of SPIE: Medical Imaging 2003: Image Processing

Year of Conference: 2003

Pages: 1757-1764

ISSN: 0277-786X

Publisher: International Society for Optical Engineering


DOI: 10.1117/12.481322