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Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

Wang Z, Xu X, Ding X, Xiao H, Huang Y, Liu J, Xing X, Wang H, Liao DJ - Comput Math Methods Med (2011)

Bottom Line: The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%.We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily.Therefore, this method is effective for the automated recognition of prostatic calculi.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology, Liuhuaqiao Hospital, Guangzhou, Guangdong, China. wangzcmail@163.com

ABSTRACT
Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

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Related in: MedlinePlus

The extracted prostate lumina contain various calculi and adhesion.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: The extracted prostate lumina contain various calculi and adhesion.

Mentions: Mathematical morphology that included expansion, corrosion, open operation and close operation and regional filling methods were used to extract the lumina completely and to prevent needless adhesion between calculus and calculi and lumina (Figures 4 and 5). The Otsu method and mathematical morphology was used to segment the suspicious calculus for further evaluation (Figure 6).


Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

Wang Z, Xu X, Ding X, Xiao H, Huang Y, Liu J, Xing X, Wang H, Liao DJ - Comput Math Methods Med (2011)

The extracted prostate lumina contain various calculi and adhesion.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3065059&req=5

fig5: The extracted prostate lumina contain various calculi and adhesion.
Mentions: Mathematical morphology that included expansion, corrosion, open operation and close operation and regional filling methods were used to extract the lumina completely and to prevent needless adhesion between calculus and calculi and lumina (Figures 4 and 5). The Otsu method and mathematical morphology was used to segment the suspicious calculus for further evaluation (Figure 6).

Bottom Line: The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%.We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily.Therefore, this method is effective for the automated recognition of prostatic calculi.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology, Liuhuaqiao Hospital, Guangzhou, Guangdong, China. wangzcmail@163.com

ABSTRACT
Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

Show MeSH
Related in: MedlinePlus