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2012 ; 35
(2
): 123-6
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Multifractal feature descriptor for histopathology
#MMPMID22101185
Atupelage C
; Nagahashi H
; Yamaguchi M
; Sakamoto M
; Hashiguchi A
Anal Cell Pathol (Amst)
2012[]; 35
(2
): 123-6
PMID22101185
show ga
BACKGROUND: Histologic image analysis plays an important role in cancer
diagnosis. It describes the structure of the body tissues and abnormal structure
gives the suspicion of the cancer or some other diseases. Observing the
structural changes of these chaotic textures from the human eye is challenging
process. However, the challenge can be defeat by forming mathematical descriptor
to represent the histologic texture and classify the structural changes via a
sophisticated computational method. OBJECTIVE: In this paper, we propose a
texture descriptor to observe the histologic texture into highly discriminative
feature space. METHOD: Fractal dimension describes the self-similar structures in
different and more accurate manner than topological dimension. Further, the
fractal phenomenon has been extended to natural structures (images) as
multifractal dimension. We exploited the multifractal analysis to represent the
histologic texture, which derive more discriminative feature space for
classification. RESULTS: We utilized a set of histologic images (belongs to liver
and prostate specimens) to assess the discriminative power of the multifractal
features. The experiment was organized to classify the given histologic texture
as cancer and non-cancer. The results show the discrimination capability of
multifractal features by achieving approximately 95% of correct classification
rate. CONCLUSION: Multifractal features are more effective to describe the
histologic texture. The proposed feature descriptor showed high classification
rate for both liver and prostate data sample datasets.