A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p =0.0002), misclassification rate (p =0.045), true negative fraction (p =0.0001) and sum of true volume fraction (p =0.0001).

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“…CAD algorithms are developed to detect breast tumours [3,4]. Several techniques for medical image segmentation exist.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

et al. 2013

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“…Verma and Zakos [47] presented a system for detection and classification of microcalcification in digital mammograms. They investigated and analyzed 14 feature extraction techniques with neural network classification.…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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“…It has been shown in [6][7] [8] that in general, feedforward ANNs can produce the breast precancerous diagnosis result almost favorably comparable with those from human experts. The applicability of ANNs combined with image processing techniques to predict the stages of breast pre-cancerous has also been carried out in [9,10,11] . The system proposed in [9] managed to achieve 92% of sensitivity out of 272 cases.…”

Section: Introductionmentioning

confidence: 99%

“…The system proposed in [9] managed to achieve 92% of sensitivity out of 272 cases. In [11] , the diagnostic system managed to achieve accuracy of 88.9% out of the 58 cases tested.…”

Section: Introductionmentioning

confidence: 99%

Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network

Ashidi¹,

Isa²,

Subramaniam³

et al. 2007

American J. of Applied Sciences

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Thirteen cytology of fine needle aspiration image (i.e. cellularity, background information, cohesiveness, significant stromal component, clump thickness, nuclear membrane, bare nuclei, normal nuclei, mitosis, nucleus stain, uniformity of cell, fragility and number of cells in cluster) are evaluated their possibility to be used as input data for artificial neural network in order to classify the breast precancerous cases into four stages, namely malignant, fibroadenoma, fibrocystic disease, and other benign diseases. A total of 1300 reported breast pre-cancerous cases which was collected from Penang General Hospital and Hospital Universiti Sains Malaysia, Kelantan, Malaysia was used to train and test the artificial neural networks. The diagnosis system which was developed using the Hybrid Multilayered Perceptron and trained using Modified Recursive Prediction Error produced excellent diagnosis performance with 100% accuracy, 100% sensitivity and 100% specificity.

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A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques

Cited by 193 publications

References 19 publications

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network

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