Characterization of Tumor Region Using SOM and Neuro Fuzzy Techniques in Digital Mammography

Ahirwar, Anamika; Jadon, R. S.

doi:10.5121/ijcsit.2011.3114

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…An overview of the literature shows that many other methods of segmentation and identification are used to detect ROI [30] [31]. In this paper, we propose method based on Level Set approach, an implicit method of deformable model, which includes edge and region proprieties.…”

Section: State Of the Artmentioning

confidence: 99%

Automatic Application Level Set Approach In Detection Calcifications In Mammographic Image

Boujelben¹,

Tmar²,

Mnif³

et al. 2011

IJCSIT

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Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours (KNN) are adopted to distinguish different ACR (American College of Radiology) classification. Moreover, we use the Digital Database for Screening Mammography (DDSM) for experiments and these results in term of accuracy varied between 60 % and 70% are acceptable and must be ameliorated to aid radiologist.

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Section: State Of the Artmentioning

confidence: 99%

Automatic Application Level Set Approach In Detection Calcifications In Mammographic Image

Boujelben¹,

Tmar²,

Mnif³

et al. 2011

IJCSIT

View full text Add to dashboard Cite

show abstract

“…To cope with this specificity, statistical approaches for texture analysis such as the moments of gray-level histogram, based on a Gray-Level Co-occurrence Matrix (GLCM), is used. It is computed to discriminate different textures in mammographic images (Oliver et al, 2007) (Zwiggelaar & .R.Denton, 2004) (Lambrou et al, 2002) (Masala et al, 2007) (Ahirwar & Jadon, 2011). In this context, Zwiggelaar et al (Zwiggelaar & .R.Denton, 2004) include some mathematic operators like translation and transportation in order to select a sub-set of features from GLCM to have a decision about tumour characterisation.…”

Section: Context Of State Of the Artmentioning

confidence: 99%

Automatic Diagnosis of Breast Tissue

Boujelben¹,

Tmar²,

Abid³

et al. 2012

Advances in Cancer Management

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“…To cope with this specificity, statistical approaches for texture analysis such as the moments of gray-level histogram, based on a Gray-Level Co-occurrence Matrix (GLCM), is used. It is computed to discriminate different textures in mammographic images(Oliver et al, 2007) (Zwiggelaar & .R.Denton, 2004) (Lambrou et al, 2002) (Masala et al, 2007) (Ahirwar & Jadon, 2011). In this context, Zwiggelaar et al(Zwiggelaar & .R.Denton, 2004) include some mathematic operators like translation and transportation in order to select a sub-set of features from GLCM to have a104Advances in Cancer Management decision about tumour characterisation.…”

mentioning

confidence: 99%

Advances in Cancer Management

Mohan¹

2012

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teaches and practices family medicine in Virginia and also works as a hospitalist physician. He became interested in research on health-related quality of life in Belgium, where he followed clinical outcomes in heart surgery patients. He later used similar methods in Virginia, to evaluate the quality of life of patients who had knee arthritis, and subsequently of patients before and after treatment of localized prostate cancer. Dr. Mohan's research has been published in over 35 high-impact factor peer-reviewed journals and he has also contributed to chapters in five books.

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Characterization of Tumor Region Using SOM and Neuro Fuzzy Techniques in Digital Mammography

Cited by 5 publications

References 15 publications

Automatic Application Level Set Approach In Detection Calcifications In Mammographic Image

Automatic Application Level Set Approach In Detection Calcifications In Mammographic Image

Automatic Diagnosis of Breast Tissue

Advances in Cancer Management

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