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AUTOMATIC KAPOSI SARCOMA DETECTION
USING TEXTURE DISTINTIVENESS
Mrs.S.Haseena, Assistant Professor,
Department of IT,
Mepco Schlenk Engineering College, Sivakasi.
Tamilnadu,India.
haseena@mepcoeng.ac.in,
Abstract— As there is a growing emphasis on skin cancer detection, Kaposi sarcoma has recently received increasing attention. Kaposi sarcoma is one deadliest form of skin cancer.
The time and costs required for dermatologists to screen all patients for Kaposi sarcoma are prohibitively expensive. There is a need for an automated system to assess a patient's risk of
Kaposi sarcoma using photographs of their skin lesions.
Dermatologists could perform diagnosis without the need of special or expensive equipment. One challenge in implementing
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K , 1 , 2 ,...... K , 1 , 2 ,...... K
Where, μ is an distribution mean and is a distribution covariance. Here, there is No closed form solution exists for5
Eqn.8 in general, so an expectation-maximization iterative algorithm is used. The expectation-maximization algorithm is initialized using cluster means, covariance and mixing proportions based on the results of the k-means clustering.
Expectation-maximization is an iterative algorithm. The initial parameters for the Gaussian mixture model are obtained from the results of the K-means clustering. That is, the initial
Gaussian means are equal to the k-means cluster means as mentioned in [1] and the distribution covariance and mixing proportions are also dependent on the cluster results. μ = μ
(10)
=
(11)
Figure 11: Learning representative texture distribution
V. EXPERIMENTAL RESULTS
In this section, we explains comparison of the proposed
TDLS algorithm and Otsu-RGB segmentation algorithm. The
Otsu segmentation technique is tested on simple RGB skin lesion image. Figure 12 and 13 shows the results perform based on TDLS and Otsu-RGB segmentation algorithm.
Image
Otsu-RGB
TDLS
Figure 12: Experimental results