New features for classification of cancerous masses in mammograms based on morphological dilation

Bojar, Konrad; Nieniewski, Mariusz

doi:10.1049/cp:20080293

Cited by 5 publications

(1 citation statement)

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“…Since benign masses are usually well circumscribed, round or oval in shape with low density and malignant masses are ill-defined, speculated with high density, researchers use shape, margin, and texture information for benignmalignant mass classification. Though a number of shape- [5][6][7] and margin- [8][9][10] based features have already been proposed for this purpose, their performance highly depends on the segmentation of masses. On the other hand, features based on textural patterns of mammograms are widely used to distinguish between benign and malignant masses [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of 2D singularities for mammographic mass classification

Rabidas

Chakraborty

Midya

2016

IET Computer Vision

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Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet‐II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet‐II coefficients and statistical co‐variates, derived from the Ripplet‐II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten‐fold cross‐validation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini‐MIAS and 200 images from the Digital Database for Screening Mammography database, respectively.

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