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Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.

Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K - Comput Math Methods Med (2015)

Bottom Line: The proposed method was compared with the k-means and fuzzy c-means clustering methods.Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists.Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

View Article: PubMed Central - PubMed

Affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

ABSTRACT
Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

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Results of focus point regions using fuzzy c-means with (a) 120 clusters and (b) 150 clusters.
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fig14: Results of focus point regions using fuzzy c-means with (a) 120 clusters and (b) 150 clusters.

Mentions: Figures 13 and 14 show sample results using k-means and fuzzy c-means clustering methods with (a) 120 clusters and (b) 150 clusters, respectively. All of the red circles indicate false positive regions, and the blue circles represent the area that, at most, needs no more than two focus point regions. However, a high concentration of overlapping focus point regions is observed using the k-means method. Figure 11 shows the same case using our proposed RPPD method with a low false positive rate. Moreover, the regions marked on the brown oval represent areas of tissue that should not be detected because they are not significant to the pathologists, and they are not reasonable areas of tissue to examine. They might represent problems in some slide preparations.


Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.

Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K - Comput Math Methods Med (2015)

Results of focus point regions using fuzzy c-means with (a) 120 clusters and (b) 150 clusters.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352493&req=5

fig14: Results of focus point regions using fuzzy c-means with (a) 120 clusters and (b) 150 clusters.
Mentions: Figures 13 and 14 show sample results using k-means and fuzzy c-means clustering methods with (a) 120 clusters and (b) 150 clusters, respectively. All of the red circles indicate false positive regions, and the blue circles represent the area that, at most, needs no more than two focus point regions. However, a high concentration of overlapping focus point regions is observed using the k-means method. Figure 11 shows the same case using our proposed RPPD method with a low false positive rate. Moreover, the regions marked on the brown oval represent areas of tissue that should not be detected because they are not significant to the pathologists, and they are not reasonable areas of tissue to examine. They might represent problems in some slide preparations.

Bottom Line: The proposed method was compared with the k-means and fuzzy c-means clustering methods.Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists.Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

View Article: PubMed Central - PubMed

Affiliation: Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

ABSTRACT
Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

Show MeSH