Texture Analysis of Breast Cancer Cells in Microscopic Images Using Critical Exponent Analysis Method

Phinyomark, Angkoon; Jitaree, S.; Phukpattaranont, Pornchai; Boonyapiphat, P.

doi:10.1016/j.proeng.2012.01.1262

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…Bauer and Mackenzie [ 10 ] found that none of the cells of the healthy patients had a D f larger than 1.28, while a large percentage of the cancer patients cells had D f >1.28. Phinyomark et al [ 8 ] found that for self‐affine surfaces with a D f <2, the surfaces are similar to the histological structures of breast cancer with a D f of 1.66. The topography of porous polystyrene is similar to the histological structures, such as cancer cells topography, and the D f agrees with those reported in the literature for cancerous structures, which suggests that is possible for the use of AFM topographies of polystyrene films with different degrees of porosity to be used as patterns in digital recognition techniques to detect cancer.…”

Section: Resultsmentioning

confidence: 99%

“…Today, modern neurosciences admit the prevalence of fractal properties in the brain at various levels of observation, from the micro‐scale to the macro‐scale, in the molecular, anatomical, functional, and pathological perspectives. Phinyomark et al [ 8 ] explored the application of fractal analysis to study the textural characteristics of microscopic images, using the critical expression analysis method to improve the ability to classify microscopic images of histologic structures from breast cancer with a complex structure. The work presented by Di Leva et al, [ 9 ] shows a holistic view of the fractal geometry of the brain and reviews the basic concepts of fractal analysis and its main applications to basic neurosciences.…”

Section: Introductionmentioning

confidence: 99%

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Fractal dimension analysis: A link between porous polystyrene films patterns and cancer cell patterns

Catarino-Centeno

Patiño‐Herrera

García‐Hernández

et al. 2022

Polymer Engineering & Sci

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To produce patterns of porous polystyrene films that can be used as standards to be compared with cancer cell patterns, thin films were made by dissolving polystyrene (PS) and polybutylmethacrylate (PBMA) in tetrahydrofuran, then PBMA phase was removed using acetic acid (AA) to generate rough surfaces. The mixture of %PS/%PBMA was formed from the following weight ratios 1/99, 5/95,…, 90/10, 99/1. The highest water contact angle (WCA) for 40/60 porous polystyrene film was (139.4 ± 1 ), exceeding the WCA of the film before removing the PBMA by 95.2 ± 0.2 ; at the same concentration, the film roughness increases 80% by removing the PBMA. The topography of porous polystyrene films is similar to that of cancer cells and the fractal dimension (D f ) of the 20/80, 30/70, 35/65, 38/62, 40/60, 43/57, 45/55, and 50/50 porous polystyrene films range between 1.6 and 2, which agrees with those reported in the literature for cancerous structures. Thus, D f data and polystyrene film topography with different porosity degrees can be used as patterns in digital recognition techniques for cancer diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractal dimension analysis: A link between porous polystyrene films patterns and cancer cell patterns

Catarino-Centeno

Patiño‐Herrera

García‐Hernández

et al. 2022

Polymer Engineering & Sci

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“…We applied the similar idea to classify BCMI into three categories, i.e. SC, LC, and cancer cells based on the FD value determined using critical exponent analysis method (Phinyomark et al ., ). However, the orientation of stromal, lymphocyte, and cancer cells causes the different FD values when they were determined using horizontal and vertical landscape signals.…”

Section: Introductionmentioning

confidence: 97%

Cell type classifiers for breast cancer microscopic images based on fractal dimension texture analysis of image color layers

et al. 2015

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Having a classifier of cell types in a breast cancer microscopic image (BCMI), obtained with immunohistochemical staining, is required as part of a computer-aided system that counts the cancer cells in such BCMI. Such quantitation by cell counting is very useful in supporting decisions and planning of the medical treatment of breast cancer. This study proposes and evaluates features based on texture analysis by fractal dimension (FD), for the classification of histological structures in a BCMI into either cancer cells or non-cancer cells. The cancer cells include positive cells (PC) and negative cells (NC), while the normal cells comprise stromal cells (SC) and lymphocyte cells (LC). The FD feature values were calculated with the box-counting method from binarized images, obtained by automatic thresholding with Otsu's method of the grayscale images for various color channels. A total of 12 color channels from four color spaces (RGB, CIE-L*a*b*, HSV, and YCbCr) were investigated, and the FD feature values from them were used with decision tree classifiers. The BCMI data consisted of 1,400, 1,200, and 800 images with pixel resolutions 128 × 128, 192 × 192, and 256 × 256, respectively. The best cross-validated classification accuracy was 93.87%, for distinguishing between cancer and non-cancer cells, obtained using the Cr color channel with window size 256. The results indicate that the proposed algorithm, based on fractal dimension features extracted from a color channel, performs well in the automatic classification of the histology in a BCMI. This might support accurate automatic cell counting in a computer-assisted system for breast cancer diagnosis.

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“…Because of its geometrical complexity and irregularity, the quanti cation of tumour morphology is approached by computational analysis and increasing involvement of arti cial intelligence 5,6 . The morphology characteristics that cannot be quanti ed visually have been commonly analysed by mathematical tools such as the fractal analysis and grey level co-occurrence matrix (GLCM) 7,8 . For instance, the fractal dimension numerically quanti es the disorder or complexity of an object, with low values indicating regular structures and high values corresponding to more irregular structures with randomness.…”

Section: Introductionmentioning

confidence: 99%

An Adapted One-dimensional Computational Approach for Irregular ROI Analysis Improves Osteosarcoma Classification

Djuričić

Ahammer

Sopta

et al. 2021

Preprint

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Background – The analysis of irregularly shaped tumour ROIs is hindered by the fact that most image analysis methods apart from first-order statistics are compatible only with rectangular ROIs. We thus aimed for the first implementation and testing of the 1-D MRI image analysis method that is fully compatible with irregular ROIs. Method – The retrospective prediction model of osteosarcoma chemoresponsiveness included T2-weighted MRI scans obtained before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Osteosarcoma morphology was quantified by calculating the one- and two-dimensional (1-D, 2-D) Higuchi dimensions (Dh), directionally and non-directionally. Results – The non-directional 1-D Dh reached a predictive AUC of 0.88, while the directional 1-D analysis along 180 radial lines robustly improved the predictive performance, reaching an AUC of 0.95, P<0.001 that is widely considered as nearly ideal. The optimal directional range was between 90o and 97o. Conclusions – We report the first validity testing of the 1-D analysis approach that is fully compatible with irregular ROIs. Such analytical adaptation to ROI shape in MRI has enhanced the osteosarcoma prediction performance over the previously reported standard 2-D analyses. The clinical importance of the early chemoresponsiveness prediction rests on its potential to prolong the survival of chemoresistant patients through personalised treatment adjustments.

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Texture Analysis of Breast Cancer Cells in Microscopic Images Using Critical Exponent Analysis Method

Cited by 12 publications

References 15 publications

Fractal dimension analysis: A link between porous polystyrene films patterns and cancer cell patterns

Fractal dimension analysis: A link between porous polystyrene films patterns and cancer cell patterns

Cell type classifiers for breast cancer microscopic images based on fractal dimension texture analysis of image color layers

An Adapted One-dimensional Computational Approach for Irregular ROI Analysis Improves Osteosarcoma Classification

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