A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation.
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Summary
The results indicate that the system is promising in improving the performance of current CAD systems by reducing FP rate while achieving relatively high sensitivity.
- Type
- article
- Published
- 2015-07-01
- Cited by
- 44
- References
- 20
- OpenAlex
- https://openalex.org/W1574905715
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:28307498
Keywords
Computer science, Preprocessor, Artificial intelligence, Pattern recognition (psychology), Mammography
References
- A completely automated CAD system for mass detection in a large mammographic database.
- A dual-stage method for lesion segmentation on digital mammograms.
- A review of automatic mass detection and segmentation in mammographic images
- Distance Regularized Level Set Evolution and Its Application to Image Segmentation
- Computer-aided detection of breast masses on full field digital mammograms.
- Computer-aided detection of breast masses: four-view strategy for screening mammography.
- Characterization of mammographic masses based on level set segmentation with new image features and patient information.
- A Concentric Morphology Model for the Detection of Masses in Mammography
- Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley’s K Function
- Assessment of a novel mass detection algorithm in mammograms.
- Texture Classification Using Refined Histogram
- Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis.
- Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation
- LIBSVM: A library for support vector machines
- Mammogram Segmentation by Contour Searching and Mass Lesions Classification With Neural Network
- Computer-Aided Diagnosis With Temporal Analysis to Improve Radiologists’ Interpretation of Mammographic Mass Lesions
- FEATURE EXTRACTION OF MAMMOGRAMS
- EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems
- Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine
- Applying Support Vector Machines to Imbalanced Datasets
Cited by
- Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern
- Mammogram classification using sparse-ROI: A novel representation to arbitrary shaped masses
- DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features
- Breast mass detection from mammography using iteration of gray-level co-occurrence matrix
- An improved method for pancreas segmentation using SLIC and interactive region merging
- A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning
- Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
- Multi-scale mass segmentation for mammograms via cascaded random forests
- Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns
- Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique
- A 3D Segmentation Method for Pulmonary Nodule Image Sequences based on Supervoxels and Multimodal Data
- Automatic psoriasis lesion segmentation in two-dimensional skin images using multiscale superpixel clustering
- A curated mammography data set for use in computer-aided detection and diagnosis research
- Superpixel texture analysis for classification of breast masses in dense background
- Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology
- Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring
- Multi-scale sifting for mammographic mass detection and segmentation
- Tumor Classification in Breast Magnetic Resonance Images (MRI) Using the Level Set–Based Segmentation Method and Gabor-Haralik Feature
- Traditional and Deep Learning Based Methods for Mammographic Image Analysis
- Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network
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