Name
Abstract | ||
|---|---|---|
Diagnosis of prostate cancer currently involves visual examination of samples for the assignment of Gleason grades using a microscope, a time-consuming and subjective process. Computer-aided diagnosis (CAD) of histopathology images has become an important research area in diagnostic pathology. This paper presents a scheme to improve the accuracy of existing CAD systems for Gleason grading on digital biopsy slides by combining color and multi-scale information using quaternion algebra. The distinguishing features of presented algorithm are: 1) use of the quaternion wavelet transform and modified local binary patterns for the analysis of image texture in regions of interest, 2) A two-stage classification method: (a) a quaternion neural network with a new high-speed learning algorithm used for multiclass classification, and (b) several binary Support Vector Machine (SVM) classifiers used for classification refinement. In order to evaluate performance, hold-one-out cross validation is applied to a data set of 71 images of prostatic carcinomas belonging to Gleason grades 3, 4 and 5. The developed system assigns the correct Gleason grade in 98.87% of test cases and outperforms other published automatic Gleason grading systems. Moreover, averaged over all the classes, testing of the proposed method shows a specificity rate of 0.990 and a sensitivity rate of 0.967. Experimental results demonstrate the proposed scheme can help pathologists and radiologists diagnose prostate cancer more efficiently and with better reproducability. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SMC.2013.199 | Systems, Man, and Cybernetics |
Keywords | Field | DocType |
two-stage classification method,correct gleason grade,gleason grading,gleason grade,multiclass classification,quaternion neural networks applied,cad system,classification refinement,automatic gleason grading system,quaternion neural network,prostate cancer gleason grading,quaternion algebra,algebra,neural nets,image texture,wavelet transforms,cancer,support vector machines,image classification | Pattern recognition,Image texture,Computer science,Local binary patterns,Quaternion,Support vector machine,Artificial intelligence,Artificial neural network,Contextual image classification,Cross-validation,Machine learning,Multiclass classification | Conference |
ISSN | Citations | PageRank |
1062-922X | 8 | 0.46 |
References | Authors | |
23 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron Greenblatt | 1 | 57 | 2.57 |
| Clara Mosquera-Lopez | 2 | 14 | 1.57 |
| Sos Agaian | 3 | 67 | 16.48 |