Benign and malignant breast tumors classification based on region growing and CNN segmentation

Rouhi, Rahimeh; Jafari, Mehdi; Kasaei, Shohreh; Keshavarzian, Peiman

doi:10.1016/j.eswa.2014.09.020

Cited by 345 publications

(162 citation statements)

References 42 publications

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“…Saki et al (2013) employed the spiculation index to represent the geometry of lesion boundary [12]. Rouhi et al (2015) uses CNN segmentation at the feature extraction stage [12]. In all these methods human segmentation is needed [12][13] [18] [19].…”

Section: Introductionmentioning

confidence: 99%

“…Rouhi et al (2015) uses CNN segmentation at the feature extraction stage [12]. In all these methods human segmentation is needed [12][13] [18] [19]. Table 1 shows the needing of human segmentation during training (learning phase) and/or using phases.…”

Section: Introductionmentioning

confidence: 99%

“…In other works, the radiologist is responsible to identify the mass edge [12][13] [18]. After edge detection, there is the study of area occupied by the mass and its neighborhood.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection and classification of masses in mammographic images in a multi-kernel approach

Lima

Silva-Filho

Santos

2016

Computer Methods and Programs in Biomedicine

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection and classification of masses in mammographic images in a multi-kernel approach

Lima

Silva-Filho

Santos

2016

Computer Methods and Programs in Biomedicine

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“…Using selected combinations of these 22 features, the classification performance was improved. Rouhi et al [63] extracted intensity, texture, and shape features from a segmented tumor. Then the GA method was used to select features and ANN was used for classification.…”

Section: Shape-based Classificationmentioning

confidence: 99%

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Yanfeng¹,

Chen²,

Chen³

et al. 2016

J Health Med Inform

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Computer-aided detection (CAD) systems can be served as a second view for radiologist. An overview of recent development in CAD methods are presented in this paper. Abnormalities detection, abnormalities classification and content-based image retrieval (CBIR) are briefly reviewed. For the abnormalities detection, micro-calcification detection, mass detection and multi-view based detection are introduced. For the abnormalities classification, micro-calcification classification and mass classification are given.

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“…As to the selection of original regions, Sainju et al (2014) selected them manually and change them by applying a segmentation method on them in the following iteration. In order to improve the accuracy of the stopping criteria, Rough et al (2015) obtained the thresholds by a trained artificial neural network (ANN). Then comes the ISODATA, it uses splitting and merging processes to estimate the number of classes.…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Kl Strategy for Estimating Number of Classes in Image Segmentation Issues

Zhao¹,

Liu²,

Zhao³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper a general entropy-KL strategy is proposed based on the disorderliness and the distances between the distributions of different classes, to estimate the number of classes in image segmentation issues. In this strategy, the information of a homogeneous region is measured by entropy. Then a region is considered to be disordered and should be split if its entropy is more than a given threshold. On the contrary, when the KL information of two homogeneous regions is less than a threshold, it is believed that they are similar and should be merged. The entropy-KL strategy can be combined with any kind of segmentation algorithm since it uses the information and distance as a general way to decide the number of classes. In this paper, the HMRF-FCM algorithm is employed as the segmentation process and combined with the entropy-KL strategy to induce a segmentation algorithm which can fix the number of classes automatically. The proposed algorithm is performed on synthetic image, real panchromatic images and SAR images to demonstrate the effectiveness.

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Benign and malignant breast tumors classification based on region growing and CNN segmentation

Cited by 345 publications

References 42 publications

Detection and classification of masses in mammographic images in a multi-kernel approach

Detection and classification of masses in mammographic images in a multi-kernel approach

A Survey of Computer-aided Detection of Breast Cancer with Mammography

An Entropy-Kl Strategy for Estimating Number of Classes in Image Segmentation Issues

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