Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Rocha, Simara Vieira da; Bráz, Geraldo; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de

doi:10.4322/rbeb.2014.008

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Rocha et al (2014) classified 200 DDSM mammogram images into normal and malignant classes using Gleason and Menhinick diversity indexes and the SVM classifier. They have reported sensitivity and specificity of 90 and 83.33%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Ibrahim

Baharudin²,

Said³

et al. 2016

American Journal of Applied Sciences

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Abstract:In this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with wellknown feature extraction method namely Bag-of-features. The resulting feature vector is fed to two different classifiers: K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of these classifiers is evaluated to use of Receiver Operating Characteristics (ROC). Our results show that k-means-max pooling, combined with K-NN, achieved good performance with an average classification accuracy of 98.19%, sensitivity of 97.09% and specificity of 99.35%.

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Section: Discussionmentioning

confidence: 99%

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Ibrahim

Baharudin²,

Said³

et al. 2016

American Journal of Applied Sciences

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“…In this example, measures of spatial autocorrelation, such as Moran's I, 60, 61 can reveal the scale and degree of dependency among observations. This is, for example, important to be useful to quantify dispersal (migration) of specific cell populations by evaluating the pH of the microenvironment.…”

Section: Landscape Ecology Applications In Pathologymentioning

confidence: 99%

Pathology to Enhance Precision Medicine in Oncology

Lloyd

Rejniak

Brown³

et al. 2015

Advances in Anatomic Pathology

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A major goal of modern medicine is increasing patient specificity so that the right treatment is administered to the right patient at the right time with the right dose. While current cancer studies have largely focused on identification of genetic or epigenetic properties of tumor cells, emerging evidence has clearly demonstrated substantial genetic heterogeneity between tumors in the same patient and within subclones of a single tumor. Thus, molecular analysis from populations of cells (either a whole tumor or small biopsy of that tumor) is, at best, an incomplete representation of the underlying biology. These observations indicate a significant need to define intratumoral evolutionary dynamics that yield the observed spatial variations in cellular properties. It is generally accepted that genetic heterogeneity among cancer cells is a manifestation of intratumoral evolution, and this is typically viewed as a consequence of random mutations generated by genomic instability within the cancer cells. We suggest that this represents an incomplete view of Darwinian dynamics, which typically are governed by phenotypic variations in response to spatial and temporal heterogeneity in environmental selection forces. We propose that pathologic feature analysis can provide precise information regarding regional variations in environmental selection forces and phenotypic adaptations. These observations can be integrated using quantitative, spatially explicit methods developed in landscape ecology to interrogate heterogenous biological processes in tumors within individual patients. The ability to investigate tumor heterogeneity has been shown to inform physicians regarding critical aspects of cancer progression including invasion, metastasis, drug resistance, and disease relapse.

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Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance

Costa

Silva

Filho

et al. 2018

Med Biol Eng Comput

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Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model. The proposed methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 93.42%, specificity of 91.21%, accuracy of 91.81%, and area under the ROC curve of 0.94. The results demonstrate the promising performance of texture extraction techniques using mean phylogenetic distance and taxonomic diversity index combined with phylogenetic trees. Graphical Abstract Stages of the proposed methodology.

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Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Cited by 4 publications

References 28 publications

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Pathology to Enhance Precision Medicine in Oncology

Classification of malignant and benign lung nodules using taxonomic diversity index and phylogenetic distance

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