An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Mass localization is important in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, we present an automated classification system for the detection of masses in mammographic images. Suspicious regions are located with an adaptive region growing firstly. Then, the initial regions are further refined with narrow band based active contour, which can improve the segmentation accuracy of masses. CLBP (Complete Local Binary Pattern) texture features are extracted from the ROIs (regions of interest) containing the segmented suspicious regions. Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist's diagnosis. The method was evaluated on a dataset with 231 images, containing 245 masses. Among them, 125 images containing 133 masses are used to optimize the parameters and are used to train SVM. The remaining 106 images are used to test the performance. It obtained 1.36 FPsI at the sensitivity 76.8%. It shows that the proposed method is a promising approach to achieve low FPsI while maintain a high sensitivity.

For the purpose of accurate and efficient mass detection in full-field digital mammograms, weproposeamethodfor automated mass detection that consists oftwostages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues). In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions. The main aim of this stage is to reduce the false-positive suspicious regions while keeping the true-positive suspicious regions. To further improve the performance of the FP reduction, the effectiveness of different transfer learning strategies is further explored and the best fine-tuning strategy in training InceptionV3 model is determined experimentally. The experimental results show that the proposed method can achieve an overall performance of 0.98 TPR @1.43 FPI on the INbreast database. In addition, the suggested segmentation method detects the mass location with 100% sensitivity and average of 5.4 false positives per image. Based on the obtained results,the introduced method was able to successfully detectand classify suspicious regions in digital mammograms and provide better TPR and FPI results in comparison with other state-of-the-art method.

Cancer is considered one of the deadliest diseases worldwide. Early detection of cancer can significantly improve patient survival rates. In recent years, computer-aided diagnosis (CAD) systems have been increasingly employed in cancer diagnosis through various medical image modalities. These systems play a critical role in enhancing diagnostic accuracy, reducing physician workload, providing consistent second opinions, and contributing to the efficiency of the medical industry. Acute lymphoblastic leukemia (ALL) is a fast-progressing blood cancer that primarily affects children but can also occur in adults. Early and accurate diagnosis of ALL is crucial for effective treatment and improved outcomes, making it a vital area for CAD system development. In this research, a CAD system for ALL diagnosis has been developed. It contains four phases which are preprocessing, segmentation, feature extraction and selection phase, and classification of suspicious regions as normal or abnormal. The proposed system was applied to microscopic blood images to classify each case as ALL or normal. Three classifiers which are Naïve Bayes (NB), Support Vector Machine (SVM) and K-nearest Neighbor (K-NN) were utilized to classify the images based on selected features. Ant Colony Optimization (ACO) was combined with the classifiers as a feature selection method to identify the optimal subset of features among the extracted features from segmented cell parts that yield the highest classification accuracy. The NB classifier achieved the best performance, with accuracy, sensitivity, and specificity of 96.15%, 97.56, and 94.59%, respectively.

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and graylevel co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified into normal, benign, and malignant types using the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach in this paper. This mammogram classification process consists of a noise filtering module, spatial-frequency transformation module, feature computation module, and classification module. The Gaussian Filtering Algorithm (GFA) is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation module. The statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this paper. Finally, Probability Histogram Segmentation Algorithm (PHSA) is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram images. This proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM datasets. From the extensive analysis of the proposed tumor detection methods stated in this work with other works, the proposed work significantly achieves a higher performance. The methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.

Ultrawideband (UWB) radar is one of the most promising emerging technologies for breast imaging. Several algorithms have already been developed which exploit dielectric contrasts between normal and malignant tissue. These algorithms have been tested on anatomically accurate models of the breast. However, the recently established similarities in dielectric properties between benign/fibroglandular and malignant tissue within the breast contribute to the occurrence of dasiafalse positivepsila results in UWB images. To mitigate the presence of these dasiafalse positivepsila results, we must investigate methods to further classify tumours based on their shape, size and texture. In this paper we review the dielectric properties of normal and malignant breast tissue and existing image algorithms. Finally, we examine methods to effectively differentiate benign/fibroglandular and malignant tissue based on shape, size and texture.

The detection of suspicious cancerous regions is still a problematic task in histopathology, where complex qualitative, and highly subjective, analyses are required by experts. Digital pathology is the option for building semi-automated tools that could assist pathologists in carrying out their analysis in a quantitative way. Methods for assisted detection of cancerous areas are mostly based on low level textural features of the tissue, whose semantic level is far from the visual appearance that histopathologists consider during their analysis. In order to bridge the semantic gap between histopathology and machine representation, we propose an algorithm for the detection of cancerous regions in lung and bladder adenocarcinoma samples, based on a supervised multi-level representation directly linked to histopathological characteristics. Instead, our unsupervised clustering method performs a segmentation of the histopathology structures according to their visual appearance through a similarity metric based on histograms of samples in the Lab perceptive color space. This permits to increase the sensitivity of the supervised approach by extending the regions (i.e., hits) it detects. We validated the accuracy of the proposed segmentation approach, using a group of ten users using 40 histopathology cases, showing a good response. The experiments, performed using the ground truth provided by a board of certified experts on different samples of adenocarcinoma (graded G1), prove the effectiveness of our approach both in terms of sensitivity and precision in detecting suspicious regions. Our algorithm is currently under testing on more samples and different cancerous histotypes.

In order to overcome the shortcomings of high false positive rate and poor generalization in the detection of microcalcification clusters regions, this paper proposes a method combining discriminative deep belief networks (DDBNs) to automatically and quickly locate the regions of microcalcification clusters in mammograms. Firstly, the breast region was extracted and enhanced, and the enhanced breast region was segmented to overlapped sub-blocks. Then the sub-block was subjected to wavelet filtering. After that, DDBNs model for breast sub-block feature extraction and classification was constructed, and the pre-trained DDBNs was converted to deep neural networks (DNN) using a softmax classifier, and the network is fine-tuned by back propagation. Finally, the undetected mammogram was inputted to complete the location of suspicious lesions. By experimentally verifying 105 mammograms with microcalcifications from the Digital Database for Screening Mammography (DDSM), the method obtained a true positive rate of 99.45% and a false positive rate of 1.89%, and it only took about 16 s to detect a 2 888 × 4 680 image. The experimental results showed that the algorithm of this paper effectively reduced the false positive rate while ensuring a high positive rate. The detection of calcification clusters was highly consistent with expert marks, which provides a new research idea for the automatic detection of microcalcification clusters area in mammograms.

In this paper, Metaheuristic Algorithms such as Genetic Algorithm (GA) and Parallel Ant Colony Optimization (ACO) are implemented to extract the suspicious region based on the asymmetry approach. In bilateral subtraction, the asymmetries between corresponding left and right breast images are considered for extracting the suspicious region from the background tissue. The breast border and the nipple position are used as reference points for alignment of mammograms. The Genetic Operators such as reproduction crossover and mutations are used to detect breast border and using a novel method called Parallel Ant Colony Optimization algorithm identifies the nipple position. The mammogram images are aligned with the help of border points and nipple position and the suspicious regions are extracted by subtracting the left and the right breast image.

This study presents a computer aided system for automatic detection and classification of breast cancer in mammogram images. First the suspicious region or the Region of Interest is identified and extracted using Intuitionistic Fuzzy C-Means Clustering technique. Next multilevel Discrete Wavelet Transformation is applied to the extracted Region of Interest. After applying Discrete Wavelet Transformation, histogram features, Gray Level Concurrence wavelet features, and wavelet energy features are extracted from each Region of Interest of the image. Before classification, Principal Component Analysis is applied on the extracted features to reduce the feature dimension. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification. The proposed system is verified with 295 mammograms in the Mammographic Image Analysis Society Database. The result shows that the proposed algorithm produces better results.

This paper proposes a image enhancement and segmentation method for mammographic images, which is based on the pixel intensity transformation, spatial filtering, edge detection, and region growing techniques. This method consists of two approaches. The first approach is applying intensity transformation, logarithmic transformation, contrast stretching, histogram equalization and spatial filtering techniques separately to enhance the mammogram images. In the second approach is applying edge detection operators, local and global thresholding techniques used to extract the suspicious regions from back ground tissue.

Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks

Breast cancer represents a significant global health challenge, ranking as one of the most prevalent malignancies among women. Early and accurate diagnosis through medical imaging is paramount for improving patient outcomes, with mammography serving as the gold standard for screening. However, the interpretation of mammograms can be challenging and subject to inter-observer variability. This study aims to comparatively evaluate the performance and computational efficiency of four prominent Convolutional Neural Network (CNN) architectures for the automated classification of breast cancer from mammogram images. Utilizing a publicly available dataset comprising 3,383 mammogram images classified as either Benign or Malignant, we trained and evaluated four distinct models: InceptionV3, DenseNet169, InceptionV4, and ResNet50. The results demonstrate that the DenseNet169 architecture achieved superior performance across all evaluated metrics, attaining the highest accuracy (73.33%), precision (70.45%), recall (67.83%), and F1-score (68.60%). Notably, DenseNet169 also exhibited the highest computational efficiency, featuring the lowest parameter count (12.49M) among the tested models. These findings suggest that DenseNet169 offers an optimal balance between diagnostic accuracy and model efficiency, positioning it as a highly promising candidate for integration into clinical decision support systems to aid radiologists in the early detection of breast cancer.

Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low contrast. This paper suggests a novel clustering approach for segmenting lesions/tumors in the mammogram images using Atanassov's intuitionistic fuzzy set theory. The algorithm initially converts an image to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. From the intuitionistic fuzzy image, two membership intervals are computed. Then, using Zadeh's min t-conorm, a new membership function is computed. Using the new membership function, an interval type 2 fuzzy image is constructed. Two types of distance functions are used in clustering-intuitionistic fuzzy divergence and a fuzzy exponential type distance function. Further, in each iteration, membership matrix is updated using a hesitation degree and a clustered image is obtained. Tumors/lesions are then segmented from the clustered image. The proposed method is compared with existing methods both quantitatively and qualitatively and it is observed that the proposed method performs better than the existing methods.

Survey of machine learning algorithms for breast cancer detection using mammogram images