CNN templates are generated using a genetic algorithm to segment mammograms.An adaptive threshold is computed in region growing process by using ANN and intensity features.In tumor classification, CNN produces better results than region growing.MLP produces the highest classification accuracy among other classifiers.Results on DDSM images are more appropriate than those of MIAS. Breast cancer is regarded as one of the most frequent mortality causes among women. As early detection of breast cancer increases the survival chance, creation of a system to diagnose suspicious masses in mammograms is important. In this paper, two automated methods are presented to diagnose mass types of benign and malignant in mammograms. In the first proposed method, segmentation is done using an automated region growing whose threshold is obtained by a trained artificial neural network (ANN). In the second proposed method, segmentation is performed by a cellular neural network (CNN) whose parameters are determined by a genetic algorithm (GA). Intensity, textural, and shape features are extracted from segmented tumors. GA is used to select appropriate features from the set of extracted features. In the next stage, ANNs are used to classify the mammograms as benign or malignant. To evaluate the performance of the proposed methods different classifiers (such as random forest, naive Bayes, SVM, and KNN) are used. Results of the proposed techniques performed on MIAS and DDSM databases are promising. The obtained sensitivity, specificity, and accuracy rates are 96.87%, 95.94%, and 96.47%, respectively.

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

The performance of segmentation methods of breast lesions is often achieved as a vigorous initiative in the automated diagnosis system using color features and mathematical morphology. However, the existing segmentation methods cannot segment the breast mass with high accuracy, particularly the mammogram images that contain the diverse textures. We propose a novel breast mass segmentation technique based on the combination of color space and intensity variation analysis. We have analyzed the properties of color space defined by the International Commission on Illumination with the focus on the visual perception of the color components. Pixel features are obtained by using a color-size histogram for textural deviation analysis as the dominant property of the mathematical morphology, which has been performed for accurate segmentation of the tumor region. This approach is tested on 513 mammograms provided by digital database for screening mammography (DDSM) and the Mammographic Image Analysis Society (MIAS). In order to assess the performance of the proposed method, both the subjective as well as objective based approaches are used. The proposed method shows the prodigious performance in the mammogram segmentation process and achieved an accuracy rate of 98.00% on MIAS and 97.00% on DDSM images.

Deviation analysis for texture segmentation of breast lesions in mammographic images

Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader
in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging
problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on
mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms
based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition
System. The proposed approach is applied to real clinical images from the full field digital mammographic
database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve
as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of
100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on
recognizing masses from a very restricted set of training regions.

/pdf/imcad-computer-aided-system-for-breast-masses-detection-fhv4npif76.pdf

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

In the sight of difficulty in segmentation of armor plate surface defects, visual attention mechanism is applied to segment defects images of steel strip surface, the key step of visual attention mechanism-fusion method of saliency im- ages is improved, thinking of the contribution of salient area size, number and distribution to the comprehensive salient image, gray level consistency and GLCM's entropy are used to calculate weight coefficients for feature images fusion, and comprehensive saliency image is obtained, then maximum entropy method is used to segment the comprehensive sa- liency image. The segmented results are compared to the results of clustering and region-growing methods, the improved visual attention mechanism has better effects.

Segmentation Algorithm of Armor Plate Surface Images Based on ImprovedVisual Attention Mechanism

Early screening of skeptical masses in mammograms may reduce mortality rate among women. This rate can be further reduced upon developing the computer-aided diagnosis system with decrease in false assumptions in medical informatics. This method highlights the early tumor detection in digitized mammograms. For improving the performance of this system, a novel bi-model processing algorithm is introduced. It divides the region of interest into two parts, the first one is called pre-segmented region (breast parenchyma) and other is the post-segmented region (suspicious region). This system follows the scheme of the preprocessing technique of contrast enhancement that can be utilized to segment and extract the desired feature of the given mammogram. In the next phase, a hybrid feature block is presented to show the effective performance of computer-aided diagnosis. In order to assess the effectiveness of the proposed method, a database provided by the society of mammographic images is tested. Our experimental outcomes on this database exhibit the usefulness and robustness of the proposed method.

Bi-model processing for early detection of breast tumor in CAD system

Since there are a lot of complex and changing characteristics of mass in mammography with great difficulty in mass segmentation, region growing become a reliable method to accomplish it. An adaptive region growing method for mass segmentation is presented so as to improve its precision and reliability and reduce the over-growing and lack-growing when dealing with different images in one principle. Background removing and region suppression are used to preprocessing the area of interest (ROI) of mass, and then it use the number of image pixels to determine the seed point for region growing, and determine whether the adaptive region growing is out of edge though the gradient distribution and tends of mass ROI in order to obtain the best growth criteria. The experimental results show that the adaptive region growing algorithm for segmentation compared to the three-terrain segmentation algorithm and model segmentation algorithm is more accurate and reliable.

Segmentation of breast masses using adaptive region growing

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Segmentation of breast masses using adaptive region growing