Semi Automatic Segmentation of Breast Thermograms Using Variational Level Set Method

Suganthi, S.; Ramakrishnan, S.

doi:10.1007/978-3-319-02913-9_59

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…These efforts can be classified into two classes: automatic segmentation of breast regions [7]] [8] and classification based on the asymmetry analysis to normal and abnormal cases [16], [17], [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the automatic segmentation [7], [8], the level set technique [9] has been used to extract the blood vessels in a thermal image. The Level set function was evolved using the gradient magnitude and direction of an edge map provided by few initial points selected in region of interest.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Level set function was evolved using the gradient magnitude and direction of an edge map provided by few initial points selected in region of interest. In [7], an automatic segmentation approach, using active contour and level set method without re-initialization, was proposed to extract the breast regions from breast thermograms. Before applying the level set, a statistical based noise removal technique and contrast limited adaptive histogram equalization were used to improve signal to noise ratio and contrast of thermal images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Once the breast region is separated from the rest of thermal images, then some features are extracted. Then some kindS of artificial classifications algorithms are applied to classify the organs in analysis as normal or abnormal [7].…”

mentioning

confidence: 99%

“…The level set function is evolved using the gradient magnitude and direction of an edge map provided by few initial points selected in region of interest. In [7], automatic segmentation approach is proposed using active contour and level set method without re-initialization. It is used to extract the breast regions from breast thermograms.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Detection of Breast Abnormalities of Thermograms based on a New Segmentation Method

Ali

Sayed

Gaber

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. Segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database. To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed. Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features. The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%). Also, using the measurements of the recall and the precision, the classification results reached 100%. This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

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