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Computer Vision Aided Measurement of Morphological Features in Medical Optics
Bogdana Bologa,Adrian Sergiu Darabant
Studia Universitatis Babes-Bolyai : Series Informatica , 2010,
Abstract: This paper presents a computer vision aided method for non invasive interupupillary (IPD) distance measurement. IPD is a morphological feature requirement in any oftalmological frame prescription. A good frame prescription is highly dependent nowadays on accurate IPD estimation in order for the lenses to be eye strain free. The idea is to replace the ruler or the pupilometer with a more accurate method while keeping the patient eye free from any moving or gaze restrictions. The method proposed in this paper uses a video camera and a punctual light source in order to determine the IPD with under millimeter error. The results are compared against standard eye and object detection routines from literature.
A COMPUTER AIDED DIAGNOSIS SYSTEM FOR DETECTION OF LUNG CANCER NODULES USING EXTREME LEARNING MACHINE
M.GOMATHI,Dr.P.THANGARAJ
International Journal of Engineering Science and Technology , 2010,
Abstract: The Computer Aided Diagnosing (CAD) system is proposed in this paper for detection of lung cancer form the analysis of computed tomography (CT) images of chest. To produce a successful Computer Aided Diagnosissystem, several problems has to be resolved. Segmentation is the first problem to be considered which helps in generation of candidate region for detecting cancer nodules. The second problem is identification of affected nodules from all the candidate nodules. Initially, the basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms are applied to the CT scan image in order to detect the lung region. Then the segmentation algorithm is applied in order to detect the cancer nodules from the extracted lung image. In this paper, Fuzzy Possibilistic C Mean (FPCM) algorithm is used for segmentation because of its accuracy. After segmentation, rule based technique is applied to classify the cancernodules. Finally, a set of diagnosis rules are generated from the extracted features. From these rules, the occurrences of cancer nodules are identified clearly. The learning is performed with the help of Extreme Learning Machine (ELM) because of its better classification. For experimentation of the proposed technique, the CT images are collected from reputed hospital. The proposed system can be able to detect the false positive nodules accurately.
Model of automated computer aided NC machine tools programming  [PDF]
J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2006,
Abstract: Purpose: Modern companies tend towards the greatest possible automation in all areas. The new control concepts of manufacturing processes required development of adequate tools for the introduction of automated control in a certain area. The paper presents such system for automated programming of CNC machine tools.Design/methodology/approach: The system is based on the previously incorporated know-how and the rules of it implementation in tool – shop. The existing manufacturing knowledge of industry tool production was collected and analysing. On this bases flow chart of all activities were made. Theoretical contribution is made in systemization of technological knowledge, which is now accessible for all workers in NC preparation units.Findings: Utilization of technology knowledge. On the basis of the recognized properties it has worked out the algorithms with which the process of manufacture, the tool and the optimum parameters selected are indirectly determined, whereas the target function was working out of the NC programme. We can first out that with information approaching of the CAM and CAPP the barriers between them, strict so far, disappear.Research limitations/implications: Till now, the system is limited to milling, drilling and similar operation. It could be extended to other machining operations (turning, grinding, wire cutting, etc.) with the same procedure. In advanced, some methods of artificial intelligence could be use.Practical implications: It is suitable for industry tools, dies and moulds production, while the system was proved in the real tool shop (production of tools for casting). The system reduces the preparation time of NC programs and could be used with any commercial available CAD/CAM/NC programming systems. Human errors are avoid or at lover level. It is important for engineers in CAD/CAM field and in tool – shops.Originality/value: The developed system is original and was not found in the literature or in the praxis. Developed method for preparation of NC programs is new and incorporate higher level of automation.
Selection of Spatiotemporal Features in Breast MRI to Differentiate between Malignant and Benign Small Lesions Using Computer-Aided Diagnosis  [PDF]
F. Steinbruecker,A. Meyer-Baese,C. Plant,T. Schlossbauer,U. Meyer-Baese
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/919281
Abstract: Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. 1. Introduction Breastcancer is one of the most common cancers among women. Contrast-enhanced MR imaging of the breast was reported to be a highly sensitive method for the detection of invasive breast cancer [1]. Different investigators described that certain dynamic signal intensity (SI) characteristics (rapid and intense contrast enhancement followed by a wash out phase) obtained in dynamic studies are a strong indicator for malignancy [2]. Morphologic criteria have also been identified as valuable diagnostic tools [3]. Recently, combinations of different dynamic and morphologic characteristics have been reported [4] that can reach diagnostic sensitivities up to 97 and specificities up to 76.5 . As an important aspect remains the fact that many of these techniques were applied on a database of predominantly tumors of a size larger than 2?cm. In these cases, MRI reaches a very high sensitivity in the detection of invasive breast cancer due to both morphological criteria as well as characteristic time-signal intensity curves. However, the value of dynamic MRI and of automatic identification and classification of characteristic kinetic curves is not well established in small lesions when clinical findings, mammography, and ultrasound are unclear. Recent clinical research has shown that DCIS with small invasive carcinoma can be adequately visualized in MRI [5] and that MRI provides an accurate estimation of invasive breast cancer tumor size, especially in tumors of 2?cm or smaller [6]. Visual assessment of
Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images  [PDF]
Hidetaka Arimura,Taiki Magome,Yasuo Yamashita,Daisuke Yamamoto
Algorithms , 2009, DOI: 10.3390/a2030925
Abstract: This paper reviews the basics and recent researches of computer-aided diagnosis (CAD) systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS), in magnetic resonance (MR) images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology, and conclude with possibilities in the future of the CAD systems for brain diseases in MR images.
Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection  [PDF]
Edén A. Alanís-Reyes, José L. Hernández-Cruz, Jesús S. Cepeda, Camila Castro, Hugo Terashima-Marín, Santiago E. Conant-Pablos
Journal of Cancer Therapy (JCT) , 2012, DOI: 10.4236/jct.2012.36132
Abstract: Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.
COMPUTER AIDED DESIGN OF A MACHINE TOOL AND DEVELOPING 3-D MODEL
NIRJHAR DEB NATH
International Journal of Engineering Science and Technology , 2012,
Abstract: POWER SCREW is an important machine tool for the following three reasons :It provides high mechanical advantage in order to move large loads with minimum effort( e.g. Screw Jack),.It generates large forces (e.g. a compactor presses) and obtains precise axial movements e.g. a machine tool leads screw. Our aim is to explore different power screw-properties, various types of power screws, designing procedure of a power screw (*we will be considering designing of a SCREW-JACK ) ,CAD (CATIA V5) model, computer program (MATLAB) to reduce the calculation time in designing considering all the design constraints and factors.
Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses  [PDF]
Laith R. Sultan, Ghizlane Bouzghar, Benjamin J. Levenback, Nauroze A. Faizi, Santosh S. Venkatesh, Emily F. Conant, Chandra M. Sehgal
Advances in Breast Cancer Research (ABCR) , 2015, DOI: 10.4236/abcr.2015.41001
Abstract: Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.
Computer Aided Interpretation Approach for Optical Tomographic Images  [PDF]
Christian D. Klose,Alexander D. Klose,Uwe Netz,Juergen Beuthan,Andreas H. Hielscher
Physics , 2010, DOI: 10.1117/1.3516705
Abstract: A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) of human finger joints in optical tomographic images. The image interpretation method employs a multi-variate signal detection analysis aided by a machine learning classification algorithm, called Self-Organizing Mapping (SOM). Unlike in previous studies, this allows for combining multiple physical image parameters, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging and inspection of optical tomographic images), were used as "ground truth"-benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances were reached when combining minimum/maximum-ratio and image variance with respect to ultra sound as benchmark. In this case, sensitivity and specificity of 0.94 and 0.96 respectively were achieved. These values are much higher than results reported when a) other classification techniques were applied or b) single parameter classifications were used, where sensitivities and specificities of 0.71 were achieved.
Computer aided planning for orthognatic surgery  [PDF]
Matthieu Chabanas,Christophe Marecaux,Yohan Payan,Franck Boutault
Physics , 2006,
Abstract: A computer aided maxillofacial sequence is presented, applied to orthognatic surgery. It consists of 5 main stages: data acquisition and integration, surgical planning, surgical simulation, and per operative assistance. The planning and simulation steps are then addressed in a way that is clinically relevant. First concepts toward a 3D cephalometry are presented for a morphological analysis, surgical planning, and bone and soft tissue simulation. The aesthetic surgical outcomes of bone repositioning are studied with a biomechanical Finite Element soft tissue model.
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