Use Of Computer-aided Diagnosis Research Articles

Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification

Background and ObjectiveThe capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images. MethodsA total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method. ResultsThe proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods. ConclusionThe proposed radiomics model has demonstrated a promising potential to synthesize and classify breast masses on ultrasound in a semi-supervised manner.

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.

Computer Aided Diagnosis System using Watershed Segmentation with Xception Based Classification Model for Lung CT Images

Early recognition and classification of pulmonary nodules by the use of computer-aided diagnosis (CAD) tools finds useful to reduce the death rate due to the illness of lung cancer. This paper devises a new CAD tool utilizing a segmentation based classification process for lung CT images. Initially, the input CT images are pre-processed by image enhancement and noise removal process. Then, watershed segmentation model is employed for the segmentation of the pre-processed images. Subsequently, the feature extraction process is carried out using Xecption model and random forest (RF) classifier is used of the identification of lung CT images as normal, benign or malignant. The use of RF model results to effective classification of the applied images. This model undergoes extensive experimentation against a benchmark lung CT image dataset and the results are investigated under several aspects. The obtained outcome pointed out the significant performance of the presented model over the compared methods.

Advanced Imaging for Barrett's Esophagus and Early Neoplasia: Surface and Subsurface Imaging for Diagnosis and Management.

Esophageal adenocarcinoma bears one of the fastest rising incidence of any cancers and generally arises in the setting of gastroesophageal reflux and Barrett's esophagus. However, early detection of neoplasia can be challenging since most patients are asymptomatic until they progress to more advanced and less curable stages, and early dysplastic lesions can be small, multifocal, and difficult to detect. Clearly, new imaging tools are needed in light of sampling error associated with random biopsies, the current standard of practice. Advances in endoscopic imaging including virtual chromoendoscopy, confocal laser endomicroscopy, and subsurface imaging with optical coherence tomography have ushered in a new era for detecting subtle neoplastic lesions. Moreover, in light of esophagus-sparing treatments for neoplastic lesions, such tools are likely to guide ablation and follow-up management. While there is no ideal single imaging modality to facilitate improved detection, staging, ablation, and follow-up of patients with dysplastic Barrett's esophagus, new advances in available technology, the potential for multimodal imaging, and the use of computer-aided diagnosis and biomarkers all hold great promise for improving detection and treatment.

Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.

Automatic Cataract Classification based on Ultrasound Technique Using Machine Learning: A comparative Study

This paper addresses the use of computer-aided diagnosis (CAD) system for the cataract classification based on ultrasound technique. Ultrasound A-scan signals were acquired in 220 porcine lenses. B-mode and Nakagami images were constructed. Ninety-seven parameters were extracted from acoustical, spectral and image textural analyses and were subjected to feature selection by Principal Component Analysis (PCA). Bayes, K Nearest-Neighbors (KNN), Fisher Linear Discriminant (FLD) and Support Vector Machine (SVM) classifiers were tested. The classification of healthy and cataractous lenses shows a good performance for the four classifiers (F-measure ≥92.68%) with SVM showing the highest performance (90.62%) for initial versus severe cataract classification.

Detecting Disease in Radiographs with Intuitive Confidence.

This paper argues in favor of a specific type of confidence for use in computer-aided diagnosis and disease classification, namely, sine/cosine values of angles represented by points on the unit circle. The paper shows how this confidence is motivated by Chinese medicine and how sine/cosine values are directly related with the two forces Yin and Yang. The angle for which sine and cosine are equal (45°) represents the state of equilibrium between Yin and Yang, which is a state of nonduality that indicates neither normality nor abnormality in terms of disease classification. The paper claims that the proposed confidence is intuitive and can be readily understood by physicians. The paper underpins this thesis with theoretical results in neural signal processing, stating that a sine/cosine relationship between the actual input signal and the perceived (learned) input is key to neural learning processes. As a practical example, the paper shows how to use the proposed confidence values to highlight manifestations of tuberculosis in frontal chest X-rays.

Algorithms for automated processing images of blood serum facies

A number of algorithms for processing images of blood serum facies are proposed for automated medical diagnosis, i.e., determination of the boundaries of facies, identification of their morphological type, and detection of certain markers indicating pathology. The mathematical modeling of such images is considered. The proposed algorithms have shown high efficiency when tested on real and simulated images and can be recommended for use in computer-aided diagnosis.

Efficient Segmentation of Medical MRI Image using WT-WS Algorithm

Image segmentation is an important processing step in many image, video and computer vision applications especially in medical field. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than the other. Up to date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of tumor detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is an important step in contouring during radiotherapy planning. This paper provides new hybrid medical image segmentation method based on Watershed and Wavelet Transform. In this paper, we propose an algorithm for segmentation problems in medical imaging modalities especially for brain Magnetic Resonance Images MRI). The watershed transformation is a useful morphological segmentation tool used for a variety of greyscale images. The segmentation procedure consists of pyramid representation, image segmentation, region merging and region projection. Each layer is split into a number of regions by rooting, labeling technique and the boundary is extracted by threshold and the image is smoothened by wavelet transform. Experimental results prove that the proposed algorithm is comparatively better than the existing systems .

Diagnosis of lymph node metastasis in uterine cervical cancer: usefulness of computer-aided diagnosis with comprehensive evaluation of MR images and clinical findings

Lymph node (LN) status is an important parameter for determining the treatment strategy and for predicting the prognosis for patients with uterine cervical cancer. Computer-aided diagnosis (CAD) can be feasible for differentiating metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer. To determine the usefulness of CAD that comprehensively evaluates MR images and clinical findings for detecting LN metastasis in uterine cervical cancer. In 680 LNs from 143 patients who underwent radical hysterectomy for uterine cervical cancer, the CAD system using the Bayesian classifier estimated the probability of metastasis based on MR findings and clinical findings. We compared the diagnostic accuracy for detecting metastatic LNs in the CAD and MR findings. Metastasis was diagnosed in 70 (12%) LNs from 34 (24%) patients. The area under ROC curves of CAD (0.924) was greater than those of the mean ADC (0.854), minimum ADC (0.849), maximum ADC (0.827), short-axis diameter (0.856) and long-axis diameter (0.753) (P < 0.05). The specificity and accuracy of the CAD (86%, 86%) were greater than those of the mean ADC (77%, 77%), maximum ADC (77%, 77%), minimum ADC (68%, 70%), and short-axis diameter (65%, 67%) (P < 0.05). CAD system can improve the diagnostic performance of MR for detecting metastatic LNs in uterine cervical cancer.

Role of Segmentation in Medical Imaging: A Comparative Study

advances in the field of medical imaging are revolutionizing medicine. The determination of the presence or severity of disease will impact clinical care for a patient or outcome status in a study. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic tools as well as with some specific algorithms. Various automatic tools have been used and discussed. The results shows that though segmentation is the crucial, required and most difficult phase yet the outcome is really advantageous in medicine for the perfect diagnosis of any disease. Both the outcomes either from automatic tool as well as using an algorithm provide the required ROI (region of interest). KeywordsUS, Region of Interest (ROI), Interstitial Lung Disease (ILD).

Evaluation of Computer-aided Diagnosis (CAD) Software for the Detection of Lung Nodules on Multidetector Row Computed Tomography (MDCT): JAFROC Study for the Improvement in Radiologists' Diagnostic Accuracy

The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis. Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules. The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis. The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.

Usefulness of Computer-Aided Diagnosis Schemes for Vertebral Fractures and Lung Nodules on Chest Radiographs

We retrospectively evaluated the usefulness of computer-aided diagnosis (CAD) schemes to radiologist performance in the simultaneous detection of vertebral fractures and lung nodules on chest radiographs. We evaluated posteroanterior and lateral chest images of 21 patients with vertebral fractures, 31 patients with lung nodules, and 10 persons acting as controls. The total number of subjects was 60 because both lesions were present in four patients. Eighteen radiologists were asked to detect vertebral fractures and nodules simultaneously on posteroanterior and lateral images. The radiologists indicated their confidence level ratings regarding the presence or absence of lesions and the most likely location of each lesion on either posteroanterior or lateral images, first without and then with CAD output. The observers' performance was evaluated with use of receiver operating characteristic (ROC) and jackknife free-response ROC curves. With the CAD scheme, the average area under the ROC curve for detection of vertebral fractures improved from 0.906 to 0.951 (p = 0.002). That for lung nodules also improved, but the improvement was not statistically significant (0.804-0.816, p = 0.297). The figure-of-merit values obtained with the jackknife free-response ROC program improved from 0.585 to 0.680 (p < 0.001) for vertebral fractures and from 0.622 to 0.650 (p = 0.017) for nodules, both results having statistical significance. Average sensitivity in the detection of lesions improved from 59.8% to 69.3% for vertebral fractures and from 64.9% to 67.6% for nodules. In the detection of vertebral fractures and lung nodules on chest images, diagnostic accuracy among radiologists improves with the use of CAD.

Diffuse boundary extraction of breast masses on ultrasound by leak plugging

We propose a semiautomated seeded boundary extraction algorithm that delineates diffuse region boundaries by finding and plugging their leaks. The algorithm not only extracts boundaries that are partially diffuse, but in the process finds and quantifies those parts of the boundary that are diffuse, computing local sharpness measurements for possible use in computer-aided diagnosis. The method treats a manually drawn seed region as a wellspring of pixel "fluid" that flows from the seed out towards the boundary. At indistinct or porous sections of the boundary, the growing region will leak into surrounding tissue. By changing the size of structuring elements used for growing, the algorithm changes leak properties. Since larger elements cannot leak as far from the seed, they produce compact, less detailed boundary approximations; conversely, growing from smaller elements results in less constrained boundaries with more local detail. This implementation of the leak plugging algorithm decrements the radius of structuring disks and then compares the regions grown from them as they increase in both area and boundary detail. Leaks are identified if the outflows between grown regions are large compared to the areas of the disks. The boundary is plugged by masking out leaked pixels, and the process continues until one-pixel-radius resolution. When tested against manual delineation on scans of 40 benign masses and 40 malignant tumors, the plugged boundaries overlapped and correlated well in area with manual tracings, with mean overlap of 0.69 and area correlation R2 of 0.86, but the algorithm's results were more reproducible.

Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography

Rationale and objectives To investigate the potential usefulness of computer-aided diagnosis as a tool for radiologists in the characterization and classification of mass lesions on ultrasound. Materials and methods Previously, a computerized method for the automatic classification of breast lesions on ultrasound was developed. The computerized method includes automatic segmentation of the lesion from the ultrasound image background and automatic extraction of four features related to lesion shape, margin, texture, and posterior acoustic behavior. In this study, the effectiveness of the computer output as an aid to radiologists in their ability to distinguish between malignant and benign lesions, and in their patient management decisions in terms of biopsy recommendation are evaluated. Six expert mammographers and six radiologists in private practice at an institution accredited by the American Ultrasound Institute of Medicine participated in the study. Each observer first interpreted 25 training cases with feedback of biopsy results, and then interpreted 110 additional ultrasound cases without feedback. Simulating an actual clinical setting, the 110 cases were unknown to both the observers and the computer. During interpretation, observers gave their confidence that the lesion was malignant and also their patient management recommendation (biopsy or follow-up). The computer output was then displayed, and observers again gave their confidence that the lesion was malignant and their patient management recommendation. Statistical analyses included receiver operator characteristic analysis and Student t-test. Results For the expert mammographers and for the community radiologists, the A z (area under the receiver operator characteristic curve) increased from 0.83 to 0.87 ( P = .02) and from 0.80 to 0.84 ( P = .04), respectively, when the computer aid was used in the interpretation of the ultrasound images. Also, the A z values for the community radiologists with aid and for the expert mammographers without aid are similar to the A z value for the computer alone (A z = 0.83). Conclusion Computer analysis of ultrasound images of breast lesions has been shown to improve the diagnostic accuracy of radiologists in the task of distinguishing between malignant and benign breast lesions and in recommending cases for biopsy.

Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs.

A computerized method for accurate determination of the ribcage boundary in chest images has been developed for use in computer-aided diagnosis (CAD) schemes for automated detection of abnormalities such as the pulmonary lung nodules, pneumothorax, interstitial disease, cardiomegaly, and interval changes in clinical chest images. With our method, the average position of the top of the lung in the chest image is determined first. Top lung edges and ribcage edges are determined within search ROIs, which are selected over top lung cages and ribcages. Three polynomial functions are applied separately to yield smooth curves for top lung edges and right and left ribcage edges. The complete ribcage boundary is then obtained by smoothly connecting three curves. A total of 1000 radiographs were digitized to 1k x 1k matrix size and a 10-bit gray scale with a laser scanner and analyzed by our method. The subjective evaluation indicated that our method produced moderately to highly accurate results in approximately 96% of the 1000 cases examined.

Localization of inter-rib spaces for lung texture analysis and computer-aided diagnosis in digital chest images.

An automated method for sampling lung textures in digital posterior/anterior chest images is being developed for use in computer-aided diagnosis of interstitial pulmonary diseases. In our present approach, two vertical profiles in the periphery of both lungs are fitted with a shift-variant sinusoidal function from which we estimate locations of posterior ribs and inter-rib spaces. Regions of interest (ROI's) for sampling lung textures are then automatically centered on the calculated locations of inter-rib spaces. In tests with 66 chest images, the overall success rate in placing 6.4 mm X 6.4 mm ROI's within inter-rib spaces with this method was 71%, with an average of 18 ROI's selected in 4-5 s/image by a VAX11/750 computer. When four additional alternative ROI's were selected on the sides of each original ROI, the success rate in having at least one ROI correctly located in an inter-rib space increased to 94%. Since we are still developing a fully automated sampling method, the present approach has been incorporated into a semiautomated method that is currently being used to sample lung textures from a large number of clinical cases.