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Computer Aided Diagnosis (CAD) is an important pattern recognition application in the field of medical sciences. Such systems assist (not replaces) doctors in the interpretation of medical image. Computerized system for automated chromosome analysis is amongst popular CAD systems which have attracted the attention of numerous researchers, making it a thrust area for further investigations. In last 25 years, extensiveinvestigations have been contributed to designAutomated Karyotyping Systems (AKS). This paper presents a comprehensive survey of the developments and current trends in the field of AKS. The survey details the overall advancements in AKS since its origin and presents an effective review of pre-processing and image enhancement techniques, segmentation methods, feature extraction algorithms and the classifiers used in AKS. Few unaddressed issues and challenges that have comparatively received meagre attention are discussed highlighting the future prospects of AKS and providing pointers to the further research.

In this paper a CAD system was designed for the classification of Protein Kinase B (PKB) using ten different discrete wavelet transforms and SSVM and SVM classifier. A set of different images has been collected from which data is divided into training and testing data set. The PKB is categorized into two classes called absent or present. The highest overall classification accuracy of 80% was obtained with biorthogonal: bior 4.4 wavelet transforms and daubechies: db6 wavelet transforms using SSVM classifier.

BackgroundOptical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images.MethodsThis was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features—blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)—were segmented from these images and used to train a new, automated classifier.ResultsOne hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%.ConclusionAutomated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.

A high-performance and accurate machining of complex special surfaces with the aid of NC equipment at the simultaneous use of four and more controlled coordinate axes is impossible without application of modern CAD systems and post-processors. Functional potentialities and a methodology for the design of complex surface machining in a "flange" part in modern CAD Esprit system are considered. Control commands are described the realization of which ensures a procedure for a computer aided design of technology, the development of a control program and simulation of operation motions of working units in a lathe. The simulation of profile surface turning is described in an intelligible way which is particularly significant for the comprehension and use of material presented.

Despite many design methodologies and tools have been developed, the presently achieved research results are not effective for design of complex products when various methods and tools in different schemes have to be employed. A new framework for optimal design of complex products is introduced in this research considering modeling, simulation and optimization aspects. First, a hybrid scheme is developed for integrated modeling of complex products. In this hybrid scheme, descriptions of a generic product are modeled by an AND-OR tree. Feasible design candidates are created from the AND-OR tree through tree-based search. Geometric descriptions in a design candidate are associated with a CAD system. Second, a hybrid simulation method is developed for evaluation of different product aspects with different simulation tools which are integrated by the hybrid modeling scheme. Simulations with geometric descriptions are conducted by analysis functions of the CAD system. Simulations with non-geometric descriptions are conducted by the knowledge-based systems. Third, a hybrid optimization method is developed to identify the optimal design of the complex product. For each design candidate, parameter optimization is conducted to obtain the optimal parameter values. The optimal design solution is identified from all design candidates through configuration optimization.

Assembly Sequence Planning (ASP) is one of the well-known optimization problems in the manufacturing industry. It represents a great deal of interest over the past three decades. This paper aims to present a method for generating ASP, taking into account the stability assignment during assembly’s operations. The developed method uses the topological and geometrical assembly constraints extracted and generated from CAD system (assembly constraints, mass and center of gravity of components, interference and contact matrices), to guarantee the generation of a feasible ASP. The main contribution of this research is the development of a method which respects the stability assignment of heavy machines during the assembly’s operations. Also, it reduces the design costs and minimizes the time of replacement or repair of failing components of industrial equipments, which has become complicated task given the complexity of today’s mechanisms. A validation example is used to highlight the advantages of the proposed approach.

The simulation of the cutting layer in the CAD system during milling internal corners of elements with complex geometry has been presented. A case of variable cutting width in the finishing process was considered and the strategy of multitransitional corner milling was analyzed.

An analysis of the accuracy of generating a solid mode based on the created surfaces is presented. In the process of creating a digital model, reconstructive engineering methods were used. The accuracy of mapping of the created solid model was determined on the basis of surface models generated by changing the tolerance value of triangles.

A chronology and a sequence of works for more than 20 years in the development of different computer program packages which have formed a basis of a future CAD system of technologies and rolling-mill equipment at the enterprise of Uralsmashzavod are presented. The descriptions of the most efficient software packages are given to solve the optimization problems in different processes of blank deformation, parameters of mechanisms and their drives, design parameters of load-bearing parts and complex units from the point of view of strength and life obtaining.

PURPOSE Accurate maxillomandibular relationship transfer is important for CAD/CAM prostheses. This study compared the 3D-accuracy of virtual model static articulation in three laboratory scanner-CAD systems (Ceramill Map400 [AG], inEos X5 [SIR], Scanner S600 Arti [ZKN]) using two virtual articulation methods: mounted models (MO), interocclusal record (IR). MATERIALS AND METHODS The master model simulated a single crown opposing a 3-unit fixed partial denture. Reference values were obtained by measuring interarch and interocclusal reference features with a coordinate measuring machine (CMM). MO group stone casts were articulator-mounted with acrylic resin bite registrations while IR group casts were hand-articulated with poly(vinyl siloxane) bite registrations. Five test model sets were scanned and articulated virtually with each system (6 test groups, 15 data sets). STL files of the virtual models were measured with CMM software. dRR , dRC , and dRL , represented interarch global distortions at right, central, and left sides, respectively, while dRM , dXM , dYM , and dZM represented interocclusal global and linear distortions between preparations. RESULTS Mean interarch 3D distortion ranged from -348.7 to 192.2 μm for dRR , -86.3 to 44.1 μm for dRC , and -168.1 to 4.4 μm for dRL . Mean interocclusal distortion ranged from -257.2 to -85.2 μm for dRM , -285.7 to 183.9 μm for dXM , -100.5 to 114.8 μm for dYM , and -269.1 to -50.6 μm for dZM . ANOVA showed that articulation method had significant effect on dRR and dXM , while system had a significant effect on dRR , dRC , dRL , dRM , and dZM . There were significant differences between 6 test groups for dRR, dRL dXM , and dZM . dRR and dXM were significantly greater in AG-IR, and this was significantly different from SIR-IR, ZKN-IR, and all MO groups. CONCLUSIONS Interarch and interocclusal distances increased in MO groups, while they decreased in IR groups. AG-IR had the greatest interarch distortion as well as interocclusal superior-inferior distortion. The other groups performed similarly to each other, and the overall interarch distortion did not exceed 0.7%. In these systems and articulation methods, interocclusal distortions may result in hyper- or infra-occluded prostheses.

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women.

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs’ edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70–0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems’ results, we used the standard ResNet50 and VGG-16 to compare our CNN’s patch-wise classification results. As well, we compared the GG’s results with that of the previous work.

The current paper presents a system for the dynamic simulation of the human hand. The simulation of the human hand offers the capability to acquire handshapes that correspond to letters of the finger alphabet, enabling an integrated representation of words and sentences. The hand model is designed using the Autodesk InventorTM and Autodesk AutoCadTM design environments. The user is able to type words or sentences which are dynamically translated into postures according to the finger alphabet. The system is based on the physiometric characteristics of an average human hand. High precision design is utilized in every part through integration of all the necessary functionalities needed to perform the movements required. The system has been tested on more than 500 words with a letter representation success rate in the range of 95–97%.

The KOMPAS-3D CAD system includes a CAM application for turning by means of a numerically controlled module. As an example, the use of this application is considered for the machining of a bush.

Pre sucasný trend vývoja, navrhovania a výroby v strojarskom priemysle je charakteristicka ich silna podpora roznymi CA technologiami. Cieľom FVT TU v Kosiciach je výchova univerzalne vzdelaných inžinierov, schopných uplatniť sa v sirokom spektre strojarskych firiem. Kombinacia odborných vedomosti, jazykových znalosti, dobrých pocitacových zrucnosti a znalosti využitia roznych CAx systemov pri rieseni technických problemov zvysuje sance absolventov skoly uplatniť sa v studovanom odbore. Implementaciou CAD/CAM/CAE technologii do procesu výucby sa studenti ucia aktivne ich využivať pri konstrukcnom a technologickom navrhovani a rieseni roznych technických problemov.

Breast cancer is one of the leading cause of death among women worldwide. Many CAD systems have been proposed for the early detection of cancer causing masses. In this paper, classification of mamm...

Healthcare providers and payers are increasingly turning to Big Data and analytics, to help them understand their patients and the context of their illnesses in more detail. Industry leaders are exploring/using Big Data to reduce costs, increase efficiency and improve patient care. The next future is an innovative approach to improving patient access using lean methods and predictive analytics. Social sciences are very much related to healthcare and both areas develop in a parallel way. In this article, we introduce one example of application: Bip4cast (a bipolar disorder CAD system). This paper shows how Bip4cast deals with different data sources to enrich the knowledge and improve predictive analysis.

Computer-Aided Diagnosis (CAD) systems for melanoma detection have received a lot of attention during the last decades because of the utmost importance of detecting this type of skin cancer in its early stages. However, despite of the many research efforts devoted to this matter, these systems are not used yet in everyday clinical practice. Very likely, this is due to two main reasons: 1) the accuracy of the systems is not high enough; and 2) they simply provide a parallel diagnosis that actually does not help to the doctors (as long as there is no way to interpret it). In this paper, we propose a novel approach that aims to provide the doctor with an enriched diagnosis. Specifically, we rely on a dermoscopic-structure-based soft segmentation to design a set of structure-specific classifiers. Each individual structure-specific classifier is trained to distinguish benign lesions from melanomas just paying attention to one type of dermoscopic structure. Then, the outputs of the individual classifiers are combined by a means of the Bayesian method that, besides the final diagnosis, provide the doctor with additional valuable information, such as the opinions of the individual structure-specific experts and the uncertainty of the diagnosis. The results in terms of the features selected for the structure-specific classifiers are consistent with the expert insights. Furthermore, regarding the automatic melanoma diagnosis problem, the proposed method has been assessed on two different datasets, and the experimental results revealed that the proposed system clearly outperforms other methods in two datasets and compares well with the official submissions of the ISBI 2016 challenge on melanoma detection. Moreover, the system performance is equivalent to that of a well-known dermoscopy expert and its combination with the human diagnosis surpasses the human performance. This work has been partially supported by the National Grant TEC2014-53390-P of the Spanish Ministry of Economy and Competitiveness. Publicado

We assessed the initial clinical performance and third-party reimbursement rates of supplementary computer-aided detection (CAD) at CT colonography (CTC) for detecting colorectal polyps 6 mm or larger in routine clinical practice.We retrospectively assessed the prospective clinical performance of a U.S. Food and Drug Administration-approved CAD system in second-reader mode in 347 consecutive adults (mean age, 57.6 years; 205 women, 142 men) undergoing CTC evaluation over a 5-month period. The reference standard consisted of the prospective interpretation by experienced CTC radiologists combined with subsequent optical colonoscopy (OC), if performed. We also assessed third-party reimbursement for CAD for studies performed over an 18-month period.In all, 69 patients (mean [± SD] age, 59.0 ± 7.7 years; 32 men, 37 women) had 129 polyps ≥ 6 mm. Per-patient CAD sensitivity was 91.3% (63 of 69). Per-polyp CAD-alone sensitivity was 88.4% (114 of 129), including 88.3% (83 of 94) for 6- to 9-mm polyps and 88.6% (31 of 35) for polyps 10 mm or larger. On retrospective review, three additional polyps 6 mm or larger were seen at OC and marked by CAD but dismissed as CAD false-positives at CTC. The mean number of false-positive CAD marks was 4.4 ± 3.1 per series. Of 1225 CTC cases reviewed for reimbursement, 31.0% of the total charges for CAD interpretation had been recovered from a variety of third-party payers.In our routine clinical practice, CAD showed good sensitivity for detecting colorectal polyps 6 mm or larger, with an acceptable number of false-positive marks. Importantly, CAD is already being reimbursed by some third-party payers in our clinical CTC practice.

Breast cancer is a serious life threat to the womanhood, worldwide. Mammography is the promising screening tool, which can show the abnormality being detected. However, the physicians find it difficult to detect the affected regions, as the size of microcalcifications is very small. Hence it would be better, if a CAD system can accompany the physician in detecting the malicious regions. Taking this as a challenge, this paper presents a CAD system for mammogram classification which is proven to be accurate and reliable. The entire work is decomposed into four different stages and the outcome of a phase is passed as the input of the following phase. Initially, the mammogram is pre-processed by adaptive median filter and the segmentation is done by GHFCM. The features are extracted by combining the texture feature descriptors Completed Local Binary Pattern (CLBP) and contourlet to frame the feature sets. In the training phase, Extreme Learning Machine (ELM) is trained with the feature sets. During the testing phase, the ELM can classify between normal, malignant and benign type of cancer. The performance of the proposed approach is analysed by varying the classifier, feature extractors and parameters of the feature extractor. From the experimental analysis, it is evident that the proposed work outperforms the analogous techniques in terms of accuracy, sensitivity and specificity.

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, thus avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: Materials consist in abdominal CT hepatic lesion from 18 patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. Several approaches are implemented to segment the region of liver and, then, detect the lesion ROI. Results: A Deep Learning approach using Convolutional Neural Network is followed for HCC grading. The obtained good results confirm the robustness of the segmentation algorithms leading to a more accurate classification.

Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (