Limits...
3D texture analysis in renal cell carcinoma tissue image grading.

Kim TY, Cho NH, Jeong GB, Bengtsson E, Choi HK - Comput Math Methods Med (2014)

Bottom Line: One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes.In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results.Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Inje University, Injero 197, UHRC, Gimhae, Gyeongnam 621-749, Republic of Korea.

ABSTRACT
One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

Show MeSH

Related in: MedlinePlus

The representative 3D renal cell carcinoma (RCC) of confocal microscopic images (a) grade 1, (b) grade 2, (c) and (d) grade 3 and grade 4, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4209774&req=5

fig1: The representative 3D renal cell carcinoma (RCC) of confocal microscopic images (a) grade 1, (b) grade 2, (c) and (d) grade 3 and grade 4, respectively.

Mentions: Noise in tissue images is generally caused by differences in the degree of dyeing, depending on the tissue thickness, and other external factors. When using a filtering method for medical image data, image degradation caused by blurring or artifacts resulting from a filtering scheme is not acceptable. To minimize these effects from the images, we applied bilateral filtering in 2 dimensions [23, 24]. It combines gray levels or colors based on both their geometric closeness and photometric similarity and prefers near values to distant values in both domain and range. It involves a weighted convolution in which the weight for each pixel depends not only on its distance from the center pixel but also on its relative intensity. In Figure 1, our developed software tool shows the representative 3D RCC visualization of the confocal microscopic images.


3D texture analysis in renal cell carcinoma tissue image grading.

Kim TY, Cho NH, Jeong GB, Bengtsson E, Choi HK - Comput Math Methods Med (2014)

The representative 3D renal cell carcinoma (RCC) of confocal microscopic images (a) grade 1, (b) grade 2, (c) and (d) grade 3 and grade 4, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4209774&req=5

fig1: The representative 3D renal cell carcinoma (RCC) of confocal microscopic images (a) grade 1, (b) grade 2, (c) and (d) grade 3 and grade 4, respectively.
Mentions: Noise in tissue images is generally caused by differences in the degree of dyeing, depending on the tissue thickness, and other external factors. When using a filtering method for medical image data, image degradation caused by blurring or artifacts resulting from a filtering scheme is not acceptable. To minimize these effects from the images, we applied bilateral filtering in 2 dimensions [23, 24]. It combines gray levels or colors based on both their geometric closeness and photometric similarity and prefers near values to distant values in both domain and range. It involves a weighted convolution in which the weight for each pixel depends not only on its distance from the center pixel but also on its relative intensity. In Figure 1, our developed software tool shows the representative 3D RCC visualization of the confocal microscopic images.

Bottom Line: One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes.In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results.Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Inje University, Injero 197, UHRC, Gimhae, Gyeongnam 621-749, Republic of Korea.

ABSTRACT
One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system.

Show MeSH
Related in: MedlinePlus