A fuzzy logic based approach for semiological analysis of microcalcifications in mammographic images

Bothorel, Sylvie; Meunier, Bruno; Muller, Serge

doi:10.1002/(sici)1098-111x(199711/12)12:11/12<819::aid-int3>3.3.co;2-h

Cited by 19 publications

(17 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of every ten women in Europe and one of every eight women in the United States are affected by breast cancer [1] and in conformity with the American National Cancer Institute, every three minutes, one woman is concerned by a breast tumor and every thirteen minutes, one woman dies due to this disease. The cause of this disease has remained unknown yet, but early detection is so significant for successful control of this cancer with low costs, and considerable decrease in the death rate [2].…”

Section: Introductionmentioning

confidence: 95%

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

2010

View full text Add to dashboard Cite

The purpose of this research was evaluating novel shape and texture feature' efficiency in classification of benign and malignant breast masses in sonography images. First, mass regions were extracted from the region of interest (ROI) sub-image by implementing a new hybrid segmentation approach based on level set algorithms. Then two left and right side areas of the masses are elicited. After that, six features (Eccentricity_feature, Solidity_feature, DeferenceArea_Hull_Rectangular, DeferenceArea_Mass_Rectangular, Cross-correlation-left and Cross-correlation-right) based on shape, texture and region characteristics of the masses were extracted for further classification. Finally a support vector machine (SVM) classifier was utilized to classify breast masses. The leave-one-case-out protocol was utilized on a database of eighty pathologically-proven breast sonographic images of patients (forty-seven benign cases and thirty-three malignant cases) to evaluate our method. The classification results showed an overall accuracy of 95.00%, sensitivity of 90.91%, specificity of 97.87%, positive predictive value of 96.77%, negative predictive value of 93.88%, and Matthew's correlation coefficient of 89.71%. The experimental results declare that our proposed method is actually a beneficial tool for the diagnosis of the breast cancer and can provide a second opinion for a physician's decision or can be used for the medicine training especially when coupled with other modalities.

show abstract

Section: Introductionmentioning

confidence: 95%

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

2010

View full text Add to dashboard Cite

show abstract

“…Such geometrical measures can typically be used in shape recognition, where geometrical attributes of the objects are taken into account (e.g. in mammography images [42,131]), or as descriptors for indexing and data mining applications.…”

Section: Intermediate Levelmentioning

confidence: 99%

Fuzzy sets for image processing and understanding

Bloch

2015

Fuzzy Sets and Systems

View full text Add to dashboard Cite

“…As mentioned in Section 2.3 we use the matrices introduced in expression (5) to calculate the differences between the colour components in a certain environment around a central pixel. For simplification we first transfer the values of these matrices from [À(2 m À 1), (2 m À 1)] (with m the amount of bits used to store a single intensity value (mostly m = 8)) into the unit interval [0, 1].…”

Section: Estimation Of the Colour Differencesmentioning

confidence: 99%

“…They also have numerous practical applications e.g. in industrial and medical image processing [5,27]. In this paper, we focus on fuzzy techniques for digital colour image filtering.…”

Section: Introductionmentioning

confidence: 99%