Automatic Computer-aided Detection Research Articles

Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability. In recent years, deep learning-based ML approaches developed to improve the diagnostic capabilities of CAD systems. This study reviews image enhancement, ML and DL methods for breast cancer detection and diagnosis using mammogram images and provides an overview of these methods. The analysis of different ways of ML and DL shows that the usages of traditional ML approaches are limited. However, DL techniques have an excellent future for implementing medical image analysis and improving the ability to exist CAD systems. Despite the significant advancements in deep learning methods for analyzing medical images to detect breast cancer, challenges still exist regarding data quality, computational cost, and prediction accuracy.

Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays

To detect and diagnosis the lungs related diseases, a Chest X-Ray (CXR) is the major tool used by the physician. Automated organ segmentation contributes to a crucial part of computer-aided detection (CAD) and diagnosis of diseases from CXRs as they enhance the accuracy in detecting, and also help in reducing the burden raised due to manual diagnosis from radiologists and medical practitioners. In this paper, an efficient automatic CAD system is proposed to detect the boundaries using a deep convolutional neural network (DCNN) model. The DCNN is trained in an end-to-end setting to facilitate fully automatic lung segmentation from anteroposterior or posteroanterior view CXRs. It learns to predict binary masks for a given CXR, by learning to discriminate regions of organ parenchyma from regions of no organ parenchyma. The proposed model’s architecture makes use of residual connections in all the concurrent up-sampling paths from each encoder block at every level, thus facilitating collective learning within blocks through inter-sharing of all high-dimensional feature maps. To generalize the proposed model to CXRs from all data distributions, image preprocessing techniques such as Top-Hat Bottom-Hat Transform and Contrast Limited Adaptive Histogram Equalization are employed. The proposed model is trained and tested using the JSRT, NLM-MC and Shenzhen Hospital datasets. The proposed method achieved a Dice Similarity Coefficient of 0.982 ± 0.018 and a Jaccard Similarity Coefficient of 0.967 ± 0.015. The implementation results demonstrated that the proposed method has surpassed the existing methods and our model is relatively lightweight and can be easily implemented on standard GPUs.

Optimized lung tumor diagnosis system using enhanced version of crow search algorithm, Zernike moments, and support vector machine.

Early detection of lung tumors is so important to heal this disease in the initial steps. Automatic computer-aided detection of this disease is a good method for reducing human mistakes and improving detection precision. The major concept here is to propose the best CAD system for lung tumor detection. In the presented technique, after pre-processing and segmentation of the lung area, its features including different orders of Zernike moments have been extracted. After features extraction, they have been injected into an optimized version of Support Vector Machine (SVM) for final diagnosis. The optimization of the SVM is based on an enhanced design of the Crow Search Algorithm (ECSA). For validating the proposed method, it was applied to three datasets including Lung CT-Diagnosis, TCIA, and RIDER Lung CT collection, and the results are validated by comparing with three state-of-the-art methods including Walwalker method, Mon method, and Naik method to indicate the system superiority toward the compared methods. The system is also analyzed based on different orders of Zernike moment to select the best order. The final results indicate that the suggested method has a suitable accuracy for diagnosing lung cancer.

Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification

Recently, automatic computer-aided detection (CAD) of COVID-19 using radiological images has received a great deal of attention from many researchers and medical practitioners, and consequently several CAD frameworks and methods have been presented in the literature to assist the radiologist physicians in performing diagnostic COVID-19 tests quickly, reliably and accurately. This paper presents an innovative framework for the automatic detection of COVID-19 from chest X-ray (CXR) images, in which a rich and effective representation of lung tissue patterns is generated from the gray level co-occurrence matrix (GLCM) based textural features. The input CXR image is first preprocessed by spatial filtering along with median filtering and contrast limited adaptive histogram equalization to improve the CXR image's poor quality and reduce image noise. Automatic thresholding by the optimized formula of Otsu's method is applied to find a proper threshold value to best segment lung regions of interest (ROIs) out from CXR images. Then, a concise set of GLCM-based texture features is extracted to accurately represent the segmented lung ROIs of each CXR image. Finally, the normalized features are fed into a trained discriminative latent-dynamic conditional random fields (LDCRFs) model for fine-grained classification to divide the cases into two categories: COVID-19 and non-COVID-19. The presented method has been experimentally tested and validated on a relatively large dataset of frontal CXR images, achieving an average accuracy, precision, recall, and F1-score of 95.88%, 96.17%, 94.45%, and 95.79%, respectively, which compare favorably with and occasionally exceed those previously reported in similar studies in the literature.

Segmentation and classification using image processing and supervising learning framework for mitosis detection in breast cancer mammographic images

Detection of the area withholding the mitotic cell growth is a vital marker in breast cancer detection. This paper aims to fabricate an automatic Computer-Aided Detection (CAD) [2] model that helps to locate the region of mitotic cell growth and signify the type of breast cancer found: benign or malignant. Contrary to the legacy literature [1], which uses partially supervised learning models applied to histopathology images, we devise a model which involves a fully supervised convolution model applied to mammographic images. The model trained with image datasets: benign and malignant breast cancer images. This model exploits the MIAS database and datasets collected privately from hospitals, consisting of mammographic images available as samples for breast cancer detection. Applying image segmentation techniques to the datasets, we highlight the region of interest, and thereby using classification methodologies, we separate the results as benign or malignant. The developed model equips us to yield results with an accuracy of 97.96% on the dataset.

A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion. In this study, a fully process-integrated approach with developing a CAD system is presented for the detection of breast masses based on texture description, spectral clustering, and Support Vector Machine (SVM). To this end, breast Regions of Interest (ROIs) are automatically detected from digital mammograms via gray-scale enhancement and data cleansing. The ROIs are segmented as labeled multi-sectional patterns using spectral clustering by the means of intensity descriptors relying on the region's histogram and texture descriptors based on the Gray Level Co-occurrence Matrix (GLCM). In the next step, shape and probabilistic features are derived from the segmented sections and given to the Genetic Algorithm (GA) to do the feature selection. The optimal feature vector comprising a fusion of selected shape and probabilistic features is submitted to linear kernel SVM for robust and reliable classification of mass tissues from the non-mass. Linear discrimination analysis (LDA) is also performed to ascertain the significance of the nominated feature space. The classification results of the proposed approach are presented by sensitivity, specificity, and accuracy measures, which are 89.5%, 91.2%, and 90%, respectively.

A fully-automated computer-aided breast lesion detection and classification system

This study presents an automatic computer-aided detection and diagnosis system which consists of two parts. The first part is for breast lesion characterization developed in pattern recognition framework (K-means clustering method) which is important to provide useful information for breast lesion characterization. Characterization of the detected lesion areas is done based on 6 parameters that are: (1) histogram, (2) shape, (3) gray level co-occurrence matrix, (4) gray level run length matrix, (5) neighboring gray tone difference matrix, and (6) gray level dependence matrix features. The second part of the system is developed based on machine learning algorithms and serves for the classification of localized breast lesions as benign and malignant. For classification, 4 different machine learning algorithms were investigated: (1) support vector, (2) k-nearest neighbors, (3) random forest, and (4) naïve Bayes classifiers. 84 histopathologically proven breast lesions were analyzed in the study. The proposed system compensates the motion artifacts, segments breast lesions, and classifies the lesions as benign and malignant. The results prove that the developed comprehensive system can detect and classifies breast lesions without any intervention. The best accuracy, sensitivity, specificity, and precision values to decide the tumor aggressiveness are 90.36%, 96.25%, 83.33%, and 92%, respectively.

This issue of ACTA

This issue of ACTA

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

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.

Automatic computer-aided analysis of optic disc pallor in fundus photographs.

Assessment of optic disc pallor in fundus photographs may be frequently misinterpreted due to the subjective nature of interpretation. We developed a fully automatic computer-aided detection (CAD) system for optic disc pallor using colour fundus photographs and evaluated the accuracy of the system. A newly proposed CAD system was developed for automated segmentation and image analysis of optic disc pallor, and a logistic regression model was developed for risk analysis. A total of 230 photographs with variable degree of optic disc pallor, and 123 normal optic discs confirmed by optical coherence tomography were tested for validation of the software. Sensitivity and specificity of the CAD system in automatic detection of optic disc pallor using colour fundus photographs were evaluated. The results of manual detection of optic disc pallor on fundus photographs by two independent ophthalmologists were compared with the efficacy of the CAD system. The fully automated CAD system achieved a sensitivity of 95.3% and a specificity of 96.7% for detecting optic disc pallor in colour fundus images. The overall accuracy of the CAD system was 96.1%, which was superior to the results of manual detection by individual examiners. We developed an automated CAD system that successfully detected optic disc pallor in fundus photographs. The proposed algorithm can assist the clinical judgement of ophthalmologists for detecting optic disc pallor in fundus photographs.

Anomaly classification in digital mammography based on multiple‐instance learning

Cancer tissues in mammography images exhibit abnormal regions; it is of great clinical importance to label a mammography image as having cancerous regions or not, perform the corresponding image segmentation. However, the detailed annotation of the cancer region is often an ambiguous and challenging task. The authors describe a fully automatic computer-aided detection and diagnosis (CAD) system to detect and classify breast cancer as malignant or benign, by using mammography and building on the multiple-instance learning (MIL) algorithms, which has been confirmed beneficial for radiologist decision sustenance. Traditional learning methods require great effort to annotate the training data by costly manual labelling and specialised computational models to detect these annotations during the test. The proposed CAD system simultaneously performs pixel-level segmentation (suspicious versus normal tissue) and image-level classification (benign versus malignant image). The set-up of the proposed system is in order: automatically segmented regions of interest (ROIs). Then, features derived from ROIs detected such as textural features and shape features are selected and extracted from each region and combined them to classify ROIs as `benign' or `malignant', by implementing MIL algorithms. Experimental results demonstrate the efficiency and robustness of the proposed CAD system compared with previous work in the literature.

Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening.

To evaluate the efficacy of a new automatic computer-aided detection (CAD) system for mass screening of retinal nerve fibre layer (RNFL) defects in a large population using fundus photographs. Among the fundus photographs of 1200 consecutive subjects who visited a healthcare centre, a total of 2270 photographs appropriate for analysis were tested. The photographs were first reviewed by two expert ophthalmologists for detection of RNFL defects (gold standard manual detection). The images were then analysed using an automatic CAD system for detecting RNFL defects in various cases of glaucomatous and non-glaucomatous optic neuropathy. A free-response receiver operating characteristics curve was generated to evaluate the validity of the CAD system. The results of the automatic detection were compared with those of manual detection, and sensitivity and specificity of the CAD system were calculated. In manual detection of 2270 photographs, 41 RNFL defects from 36 photographs (1.6%) were detected, and no RNFL defects were found in 2234 photographs (98.4%). The sensitivity of the CAD system for detecting RNFL defects was 90.2% (37/41 RNFL defects) and the specificity was 72.5% (1620/2234 photographs with no RNFL defects) at a false-positive rate of 0.36 per image. The new CAD system successfully detected RNFL defects during mass screening of fundus photographs in a large population who visited a healthcare centre. The proposed algorithm can be useful for clinicians in screening RNFL defects in healthcare centres. The false-positive rate is still unsatisfactory, although improved compared with the previous study. Further studies are needed to enhance the speed and specificity of image analysis using the CAD system.

Treatment Response Evaluation of Breast Cancer after Neoadjuvant Chemotherapy and Usefulness of the Imaging Parameters of MRI and PET/CT.

This study was aimed to evaluate the ability of imaging parameters measured on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted MRI (DWI) and positron emission tomography/computed tomography (PET/CT) to serve as response markers in breast cancer after neoadjuvant chemotherapy (NAC). In 20 patients with breast cancer, DCE-MRI and DWI using a 3 T scanner and PET/CT were performed before and after NAC. DCE-MRI was analyzed using an automatic computer-aided detection program (MR-CAD). The response imaging parameters were compared with the pathologic response. The areas under the curve (AUCs) for DCE-MRI using MR-CAD analysis, DWI and PET/CT were 0.77, 0.59 and 0.76, respectively. The combination of all parameters measured by MR-CAD showed the highest diagnostic performance and accuracy (AUC = 0.77, accuracy = 90%). The combined use of the parameters of PET/CT with DCE-MRI or DWI showed a trend toward improved specificity and negative predictive value (100%, 100%, accuracy = 87.5%). The use of DCE-MRI using MR-CAD parameters indicated better diagnostic performance in predicting the final pathological response compared with DWI and PET/CT, although no statistically significant difference was observed. The combined use of PET/CT with DCE-MRI or DWI may improve the specificity for predicting a pathological response.

Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG).

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).

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.

A Pipeline for Computer Aided Polyp Detection

We present a novel pipeline for computer-aided detection (CAD) of colonic polyps by integrating texture and shape analysis with volume rendering and conformal colon flattening. Using our automatic method, the 3D polyp detection problem is converted into a 2D pattern recognition problem. The colon surface is first segmented and extracted from the CT data set of the patient's abdomen, which is then mapped to a 2D rectangle using conformal mapping. This flattened image is rendered using a direct volume rendering technique with a translucent electronic biopsy transfer function. The polyps are detected by a 2D clustering method on the flattened image. The false positives are further reduced by analyzing the volumetric shape and texture features. Compared with shape based methods, our method is much more efficient without the need of computing curvature and other shape parameters for the whole colon surface. The final detection results are stored in the 2D image, which can be easily incorporated into a virtual colonoscopy (VC) system to highlight the polyp locations. The extracted colon surface mesh can be used to accelerate the volumetric ray casting algorithm used to generate the VC endoscopic view. The proposed automatic CAD pipeline is incorporated into an interactive VC system, with a goal of helping radiologists detect polyps faster and with higher accuracy.