title: Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies.
abstract: Grading of breast cancer malignancy is a key step in its diagnosis, which in turn helps to determine its prognosis and a course of treatment. In this paper, we consider the application of pattern recognition and image processing techniques to perform computer-assisted automatic breast cancer malignancy grading from cytological slides of fine needle aspiration biopsies. To determine a classification of the malignancy of the slide, a feature set is first determined from imagery of the slides. In this paper we investigated the nature of a wide set of features extracted from biopsy images to determine their discriminatory power and cross-correlation. Feature vector reduction is studied using a correlation map of the features, determining discriminatory power using the Kolmogorov-Smirnov test, significant feature selection, and stepwise feature selection. The reduction of the feature vector simplifies the complexity of classification scheme and does not impair the classification accuracy. In some cases a decrease of the error rate is noted. Based on this analysis, we present an improved classification system for cancer malignancy grading.
authors: Department of Computer Engineering, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland. Electronic address: lukasz.jelen@pwr.edu.pl.
authors: Łukasz
authors: Ł
authors: Jeleń
authors: Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montréal, Québec, Canada H3G 1M8.
authors: Adam
authors: A
authors: Krzyżak
authors: Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montréal, Québec, Canada H3G 1M8.
authors: Thomas
authors: T
authors: Fevens
authors: Department of Pathology and Oncological Cytology, Medical University of Wrocław, Borowska 213, 50-556 Wrocław, Poland.
authors: Michał
authors: M
authors: Jeleń
mesh_terms: D044963:Biopsy, Fine-Needle; D001943:Breast Neoplasms / Q000737:chemistry / Q000145:classification / Q000175:diagnosis*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006651:Histocytochemistry; D006801:Humans; D008853:Microscopy; D060787:Neoplasm Grading; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Breast cancer; Cancer classification; Computer-aided diagnosis; Malignancy grading; Pattern analysis