Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of orthonormal multi-wavelets
Applied Numerical Mathematics - Special issue on selected keynote papers presented at 14th IMACS World Congress, Atlanta, NJ, July 1994
Pattern recognition and image analysis
Pattern recognition and image analysis
Similarity measurement method for the classification of architecturally differentiated images
Computers and Biomedical Research
Digital Image Processing
| Hi-index | 0.00 |
Prostatic biopsies provide the information for the determined diagnosis of prostatic cancer. Computer-aid investigation of biopsies can reduce the loading of pathologists and also inter- and intra-observer variability as well. In this paper, we proposed a two stages approach for prostatic cancer grading according to Gleason grading system. The first stage classifies biopsy images into clusters based on their Skeleton-set (SK-set), so that images in the same cluster consist of the similar two-tone texture. In the second stage, we analyzed the fractal dimension of sub-bands derived from the images of prostatic biopsies. We adopted the Support Vector Machines as the classifier and using the leaving-one-out approach to estimate error rate. The present experimental results demonstrated that 92.1% of accuracy for a set of 1000 pathological prostatic biopsy images.