Bio: R Sathyaraj is an academic researcher. The author has contributed to research in topics: Image segmentation & Segmentation-based object categorization. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.
TL;DR: Four MRI segmentation algorithms that are selected from literature survey are reviewed and the reviewed methods have altered the objective function of traditional FCM and also unified its spatial information in the objectivefunction.
Abstract: An authentic and up to-date analysis in case of any disease is basic demand in the field of medical sciences as this might escalate the probability of endurance of a human. The role of image segmentation is important for most tasks demanding image analysis. Various image segmentation techniques are being utilized for diagnosis of medical images. However authentic segmentation of MRI (Medical Resonance Image) is of grave importance for exact analysis by computer assisted clinical apparatus. Our paper gives a survey of recent MRI segmentation approaches that uses FCM algorithm. In this paper, we have reviewed four MRI segmentation algorithms that are selected from literature survey. To overcome the flaws of traditional FCM, the reviewed methods have altered the objective function of traditional FCM and also unified its spatial information in the objective function.
TL;DR: An algorithm called as rough k means clustering algorithm for segmentation, which is applying an oppositional fruit fly algorithm to develop an effectiveness of the Gabor filter, is introduced.
Abstract: From the classifications, an effective brain tumor classification and segmentation is the curious part for identifying the tumor and non-tumor cells in brain and the cell levels are evaluated. The brain tumor segmentation and classification is established on their experiences. The accuracy of tumor segmentation is very crucial to diagnosis accuracy. So, in our work we are align and improve an approach for tumor identification applying brain MR image segmentation. With an efficient, accurate and reproducible manner, the aim of our suggested method is to evaluate the tumor. Then the brain tumor is separated by using the effective techniques. For segmentation process, first the MRI image must be preprocessed. Next, the process of feature extraction is done by using preprocessed images. In feature extraction process, a raised Gabor wavelet transform (IGWT) is applied. In this research, the means of optimization technique is changed from the traditional Gabor wavelet transform. And the effectiveness of that optimization technique is aligned by using an oppositional fruit fly algorithm. At the end of the process, feature values are transferred in to the clustering process for segmentation. In this article we are introduced an algorithm called as rough k means clustering algorithm for segmentation. Here, we are applying an oppositional fruit fly algorithm to develop an effectiveness of the Gabor filter. Further to raise the classification accuracy of brain tumor we are introduced a multi kernel support vector machine algorithm.
TL;DR: The experimental results show that the proposed method attained better results compared to existing work and the performance of the proposed methodology is evaluated in terms of Sensitivity, Specificity, and Accuracy.
Abstract: Image processing is significant in the medical field which provides detailed information about medical images and image segmentation is an essential part of medical image processing. In the medical field, various modalities have been utilized such as X-ray, CT scan and MRI, etc. MRI provides accurate results than other techniques. Our proposed technique is highly focused on tumor identification using MRI image segmentation. The proposed methodology consists of five stages namely, pre-processing, feature extraction, feature selection, classification, and segmentation. Initially, input MRI images are given to the preprocessing stage to fit the images for further processing. In this preprocessing phase, the input images are converted into a transform domain with the aid of Improved Gabor Wavelet Transform (IGWT). Then, GLCM related features are extracted and important features are selected with the help of the Oppositional fruit fly algorithm (OFFA). Then, the selected features are given to the support vector machine (SVM) classifier to classify an image as normal or abnormal. After the classification process, the abnormal images are selected and given to the segmentation process. For segmentation, in this paper, we utilized an effective rough k-means algorithm. The performance of the proposed methodology is evaluated in terms of Sensitivity, Specificity, and Accuracy. The experimental results show that our proposed method attained better results compared to existing work.
TL;DR: A success rate about 90% was observed in semantic segmentation of the multiform femoral head and proximal femur bones in a total of 194 MRI sections obtained from 33 MRI sequences of 13 patients with deep CNNs.
TL;DR: Based on the development of artificial intelligence technology and the combination of data mining and XBRL technology, this paper discusses the new strategies of contemporary management accounting development.
Abstract: In today’s society, the application of information technology is becoming more and more extensive. At the same time, management accounting, as an important branch of modern accounting, also ushered in new development opportunities, and the research of data mining also pays more attention to the combination of theory and practice. Therefore, data mining can provide some technical support for the implementation of strategic management accounting. Because the most important thing of management accounting informatization is to process, calculate and transmit the business information of the enterprise through the corresponding information processing platform through the use of computer technology, and provide the corresponding data to the management of the major companies in order to better analyze and make decisions and perfect the future development strategy of the enterprise, so the screening of the corresponding technology is more important in the process of management accounting informatization. Based on the development of artificial intelligence technology and the combination of data mining and XBRL technology, this paper discusses the new strategies of contemporary management accounting development.
TL;DR: Through the application of virtual reality technology in the automobile design stage, the interactive and network-based remote research on automobile modeling will also make the automobileDesign process more convenient, easier to communicate with designers, and reduce the development cycle and cost of automobile design.
Abstract: Automobile styling design is an important part of the design chain. In the traditional automobile modeling evaluation, the process of project evaluation is more in-depth, and designers exchange ideas. Different designers have different evaluations of automobile styling. The evaluation process lasts a long time, which leads to the design cycle being too long and the efficiency of automobile modeling evaluation is greatly reduced. The introduction of virtual reality in automobile modeling evaluation can effectively optimize the evaluation process and promote the rapid adjustment of the model on the basis of development. From the virtual reality system based on mechanical engineering, we only need the parameters of the car model to observe the actual situation through VR technology, and use the measurement tools to directly and accurately evaluate the driver’s field of vision. Through the application of virtual reality technology in the automobile design stage, the interactive and network-based remote research on automobile modeling will also make the automobile design process more convenient, easier to communicate with designers, and reduce the development cycle and cost of automobile design.