Automated classification of renal cell carcinoma subtypes using scale invariant feature transform.
The task of analyzing tissue biopsies performed by a pathologist is challenging and time consuming. It suffers from intra- and inter-user variability. Computer assisted diagnosis (CAD) helps to reduce such variations and speed up the diagnostic process. In this paper, we propose an automatic computer assisted diagnostic system for renal cell carcinoma subtype classification using scale invariant features. We capture the morphological distinctness of various subtypes and we have used them to classify a heterogeneous data set of renal cell carcinoma biopsy images. Our technique does not require color segmentation and minimizes human intervention. We circumvent user subjectivity using automated analysis and cater for intra-class heterogeneities using multiple class templates. We achieve a classification accuracy of 83% using a Bayesian classifier.
Raza
S
S
Georgia Institute of Technology, Atlanta, GA 30332, USA. sraza3@gatech.edu
Sharma
Yachna
Y
Chaudry
Qaiser
Q
Young
Andrew N
AN
Wang
May D
MD
D000465:Algorithms; D001331:Automation; D002292:Carcinoma, Renal Cell / Q000145:classification* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007680:Kidney Neoplasms / Q000145:classification* / Q000473:pathology; D014584:User-Computer Interface