文件名称:1-s2.0-S1877050915001751-main
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Nowadays prostate disease is very common in adult and elderly men. Since all types of prostate diseases are having similar
symptoms, it is difficult to diagnose malignant prostate at an early stage. In this work an attempt is made to identify the types of
prostate diseases abdomen CT images of the patients using texture analysis. Prostate region is segmented the CT
image slice. Texture features are extracted the segmented images using an evolving transform named Sequency based
Mapped Real Transform (SMRT). Six different SMRT feature sets are derived by varying sub image size and block size. Each
feature set is optimized using Genetic Algorithm (GA).-Nowadays prostate disease is very common in adult and elderly men. Since all types of prostate diseases are having similar
symptoms, it is difficult to diagnose malignant prostate at an early stage. In this work an attempt is made to identify the types of
prostate diseases abdomen CT images of the patients using texture analysis. Prostate region is segmented the CT
image slice. Texture features are extracted the segmented images using an evolving transform named Sequency based
Mapped Real Transform (SMRT). Six different SMRT feature sets are derived by varying sub image size and block size. Each
feature set is optimized using Genetic Algorithm (GA).
symptoms, it is difficult to diagnose malignant prostate at an early stage. In this work an attempt is made to identify the types of
prostate diseases abdomen CT images of the patients using texture analysis. Prostate region is segmented the CT
image slice. Texture features are extracted the segmented images using an evolving transform named Sequency based
Mapped Real Transform (SMRT). Six different SMRT feature sets are derived by varying sub image size and block size. Each
feature set is optimized using Genetic Algorithm (GA).-Nowadays prostate disease is very common in adult and elderly men. Since all types of prostate diseases are having similar
symptoms, it is difficult to diagnose malignant prostate at an early stage. In this work an attempt is made to identify the types of
prostate diseases abdomen CT images of the patients using texture analysis. Prostate region is segmented the CT
image slice. Texture features are extracted the segmented images using an evolving transform named Sequency based
Mapped Real Transform (SMRT). Six different SMRT feature sets are derived by varying sub image size and block size. Each
feature set is optimized using Genetic Algorithm (GA).
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