Hybrid Segmentation of Mass in Mammograms Using Template Matching and Dynamic Programming

Song, Enmin; Xu, Shengzhou; Xu, Xiangyang; Zeng, Jianye; Lan, Yihua; Zhang, Shenyi; Hung, Chih‐Cheng

doi:10.1016/j.acra.2010.07.008

Cited by 31 publications

(22 citation statements)

References 28 publications

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“…The DDSM provide delineations of mass regions. However, precision of such delineations is not adequate for validation in our approach, since it was done on downscaled images of DDSM database (by factor of 8) [41], see Section 2. Therefore, we wore based in the extraction of ROI's on manual segmentation entertained by two expert radiologists of more than 30 years of clinical experience from the Farabi imaging.…”

Section: Resultsmentioning

confidence: 99%

A New GLLD Operator for Mass Detection in Digital Mammograms

Gargouri

Masmoudi

et al. 2012

International Journal of Biomedical Imaging

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During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to be A z = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.

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Section: Resultsmentioning

confidence: 99%

A New GLLD Operator for Mass Detection in Digital Mammograms

Gargouri

Masmoudi

et al. 2012

International Journal of Biomedical Imaging

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“…is the local similarity measure between pixel i and j th neighbour pixel in the local window. (5) where denotes the local spatial relationship between i and j. denotes the local gray-level relationship. makes the influence of the pixels within the local window change flexible according to their distance from the central pixel and thus more local information can be used.…”

Section: Fuzzy Clustering With Spatial Informationmentioning

confidence: 99%

“…al., [4] proposed a segmentation approach which uses plane fitting based on dynamic programming optimization approach. Later a combination of template matching with dynamic programming developed that perform better than plane fitting [5].…”

Section: Introductionmentioning

confidence: 99%

A spatial fuzzy based level set method for mammogram mass segmentation

Anitha

Peter

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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The evolution of level set segmentation needs an appropriate initialization and controlling parameters, which requires manual intervention. A new spatial fuzzy level set algorithm is proposed in this paper to facilitate the automatic mammogram image segmentation. The initial segmentation of the mass is obtained by spatial fuzzy clustering that incorporates the spatial information and local intensity information to compute the weighted summed image. The maximum membership cluster extracted from the fuzzy clustering is used as an initial contour for the level set segmentation that is used to refine the mass boundary. The control parameters of level set algorithm also estimated from the results of fuzzy clustering. Experimental results show that the proposed method gives promising results to effectively segment the mass regions in the mammograms.

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“…Five out of those 10 papers used the Overlap measure to evaluate the segmentation system [8,9,[24][25][26]. This measure is the relative intersection area between two regions considered.…”

Section: Evaluation Of Segmentationmentioning

confidence: 99%

Applying graphical oracles to evaluate image segmentation results

2017

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Segmentation plays an important role in the pattern recognition and image processing areas. Several techniques have been proposed aiming at solving generic issues or particular applications. Traditionally, these techniques have been evaluated by using the Overlap measure, which verifies the coincident and non-coincident areas between the image resulting from a segmentation process and an image considered correct. Albeit widely, this type of measure does not allow flexibility in the assessment process. We here propose an approach to evaluate segmentation techniques using concepts from content-based image retrieval and considering a methodology for testing generic programs with graphical outputs, named graphic oracle. Our approach was applied to evaluate the segmentation of mammographic images, and the results indicate a performance compatible with the traditional measure with more flexibility and precision. Thus, our approach provides a contribution to allow a more flexible segmentation assessment, according to image characteristics and application objectives.

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Hybrid Segmentation of Mass in Mammograms Using Template Matching and Dynamic Programming

Cited by 31 publications

References 28 publications

A New GLLD Operator for Mass Detection in Digital Mammograms

A New GLLD Operator for Mass Detection in Digital Mammograms

A spatial fuzzy based level set method for mammogram mass segmentation

Applying graphical oracles to evaluate image segmentation results

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