The goal of this paper is to introduce a new fuzzy local iterative algorithm that matches local color statistics of a
reference image to the distribution of the input image. Reference images are considered to have a desirable color
distribution for a specific application. The proposed algorithm consists of three stages: (1) images clustering by fuzzy cmeans,
(2) clusters’ matching, and (3) color distribution transfer between the matching clusters. First, a color similarity
measurement is used to segment image regions in the reference and input images. Second, we match the most similar
clusters in order to avoid the appearing of undesirable artifacts due to differences in the color dynamic range. Third, the
color characteristics of the reference clusters are transferred to the equivalent clusters in the input image by applying an
iterative process. The new image normalization tool has several advantages: it is computationally efficient and it has the
potential of increasing substantially the accuracy of segmentation and classification systems based on analysis of color
features. Computer simulations indicate that the iterative and gradual color matching procedure is able to standardize the
appearance of color images according to a desirable color distribution and reduce the amount of artifacts appearing in the
resulting image.
Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital
pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on
digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological
images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to
Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use
pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is
estimated through three different cross-validation schemes. The proposed system offers the potential for automating
classification of histological images and supporting prostate cancer diagnosis.
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