Title :
Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation
Author :
Moftah, Hossam ; Ibrahim, Mohammad ; Hassanien, Aboul Ella ; Schaefer, Gerald
Author_Institution :
Fac. of Comput. & Inf., Beni Suef Univ., Beni Suef, Egypt
Abstract :
Mammary gland tumors are among the most common tumors in cats. Over 85 percent of mammary tumors in cats are malignant and they tend to grow and metastasize quickly to different organs like lungs and lymph nodes. Similar to breast tumors in humans, they start as a small lump in a mammary gland and then grow and increase in size unless detected and treated. In this paper, we present an approach to detect broadenoma mammary gland tumors in cats using ant colony optimisation. Image features can then be extracted from the segmented image regions. To evaluate the performance of our presented approach, 25 microscopical images were taken from tissue slides of broadenomas from three cat cases. The experimental results obtained confirm that the effectiveness and performance of the proposed system is high.
Keywords :
ant colony optimisation; feature extraction; image segmentation; lung; tumours; zoology; ant colony optimisation; breast tumors; broadenoma mammary gland tumors; cats; image feature extraction; lungs; lymph nodes; mammary gland tumor detection; metastasize; microscopical images; segmented image regions; tissue slides; Accuracy; Cats; Feature extraction; Image segmentation; Mammary glands; Microscopy; Tumors; ant colony optimisation; clustering; mammary gland tumor; medical imaging;
Conference_Titel :
Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on
Conference_Location :
Naha
DOI :
10.1109/ACPR.2013.173