An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

Kashyap, Kanchan Lata; Bajpai, Manish Kumar; Khanna, Pritee

doi:10.1007/s11042-017-4751-5

Cited by 20 publications

(4 citation statements)

References 36 publications

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“…Shape analysis has been widely studied and its usefulness has already been demonstrated in many different problems, such as lesion detection [ 22 ], classification [ 22 – 24 ], survival analysis [ 1 , 25 ], and tissue segmentation [ 1 , 7 , 8 ], but the lack of complete shape analysis tools in the R environment motives our work. Although there are tools available in CRAN and Bioconductor, none have a full pipeline [ 26 ], support as many shape features and representations [ 27 ], or have applications to medical imaging [ 28 ] as our tool.…”

Section: Discussionmentioning

confidence: 99%

SAFARI: shape analysis for AI-segmented images

et al. 2022

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Background Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. Results We developed SAFARI (shape analysis for AI-segmented images), an open-source package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I–IV lung cancer and another case study on 61 glioblastoma patients. Conclusions SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/.

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Section: Discussionmentioning

confidence: 99%

SAFARI: shape analysis for AI-segmented images

et al. 2022

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“…A Dual stage adaptive thresholding (DuSAT) [3] has been successfully applied to selected mass regions in mammograms. For removal of background, Thresholding is applied in [21]. [20] have proposed intensity and gradient based method with Abnormality detection classifier (ADC) for the classification of normal and abnormal mammograms.…”

Section: Comparison With Existing Algorithmsmentioning

confidence: 99%

Unsupervised Method to Localize Masses in Mammograms

2021

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Breast cancer is one of the most common and prevalent type of cancer that mainly affects the women population. chances of effective treatment increase with early diagnosis. Mammography is considered one of the effective and proven technique for early diagnosis of breast cancer. Tissues around masses look identical in mammogram, which makes automatic detection process a very challenging task; they are indistinguishable from the surrounding parenchyma. In this paper, we present an efficient and automated approach to segment masses in mammograms. The proposed method uses hierarchical clustering to isolate the salient area followed by extraction of features to reject false detection. We applied our method on two popular publicly available data-sets (mini-MIAS and DDSM). A total of 56 images from minimias database, and 76 images from DDSM were randomly selected. Results are explained in-terms of ROC (Receiver Operating Characteristics) curves and compared with other state of the art techniques. Experimental results demonstrate the efficiency and advantages of the proposed system in automatic mass identification in mammograms.

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“…The best area under curve (AUC) values of 0.9439 and 0.9615 were achieved with the proposed feature selection technique and SVM classifier with ten-fold cross-validation and leave-one-out scheme, respectively. Kashyap et al [ 18 ] proposed a CAD system for the diagnosis of breast masses in mammograms and their shape analysis. The fast fuzzy C-means clustering algorithm was employed for the extraction of mass lesions from pre-processed mammograms.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

Singh

Sharma

Singh

2022

Vis. Comput. Ind. Biomed. Art

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This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms. An improved machine learning-based framework was developed for this study. The proposed system was tested using 106 full field digital mammography images from the INbreast dataset, containing a total of 115 breast mass lesions. The proficiencies of individual and various combinations of computed textures and geometric features were investigated by evaluating their contributions towards attaining higher classification accuracies. Four state-of-the-art filter-based feature selection algorithms (Relief-F, Pearson correlation coefficient, neighborhood component analysis, and term variance) were employed to select the top 20 most discriminative features. The Relief-F algorithm outperformed other feature selection algorithms in terms of classification results by reporting 85.2% accuracy, 82.0% sensitivity, and 88.0% specificity. A set of nine most discriminative features were then selected, out of the earlier mentioned 20 features obtained using Relief-F, as a result of further simulations. The classification performances of six state-of-the-art machine learning classifiers, namely k-nearest neighbor (k-NN), support vector machine, decision tree, Naive Bayes, random forest, and ensemble tree, were investigated, and the obtained results revealed that the best classification results (accuracy = 90.4%, sensitivity = 92.0%, specificity = 88.0%) were obtained for the k-NN classifier with the number of neighbors having k = 5 and squared inverse distance weight. The key findings include the identification of the nine most discriminative features, that is, FD26 (Fourier Descriptor), Euler number, solidity, mean, FD14, FD13, periodicity, skewness, and contrast out of a pool of 125 texture and geometric features. The proposed results revealed that the selected nine features can be used for the classification of breast masses in mammograms.

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An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

Cited by 20 publications

References 36 publications

SAFARI: shape analysis for AI-segmented images

SAFARI: shape analysis for AI-segmented images

Unsupervised Method to Localize Masses in Mammograms

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

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