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A practical structural representation of a segmented image

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1996年第6期E79D卷 873-880页

作者： He, SJ Abe, N Tan, CL Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka-shi 820 Japan

A practical structural representation of a segmented image is presented. The practicalness is defined according to whether or not: the representation can be directly generated from its corresponding segmented image. Two structural representations have been proposed in the literature. They are hierarchical structure and relational graph. Because they are defined totally on the basis of human perception, neither of the representations can be directly generated from the corresponding segmented image. The structural representation described in this paper, however, is based on the relations among pattern primitives and generated by applying some human-oriented constraints.

关键词： structural representation segmented image generalized connected component (GCC's)

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Implementation of 3D Stereo image Capture System Based on Multi-segmented Method

Implementation of 3D Stereo Image Capture System Based on Mu...

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15th World Multi-Conference on Systematics, Cybernetics and Informatics (WMSCI 2011)

作者： Ham, Woonchul Kwon, Hyeokjae Enkhbaatar, T. Chonbuk Natl Univ Div Elect & Informat Engn Jeonju South Korea

ISBN: (纸本)9781936338290

In this paper, we suggest a new modeling of CMOS camera by using combinations of several pinhole camera models and check its validity by using synthesized stereo images based on OpenGL software. We introduce 3D image capturing hardware system built in my laboratory which consists of 5 motor controller and two CMOS camera module based on S3C6410 processor. We choose 9 segmentations and propose the method to find optimal alignment and focusing based on the measure of alignment and sharpness and propose the synthesizing fusion with the optimized 9 segmentation images for the best 3D depth feeling. From the experimental results concerning disparity values in each 9 segments, we can assure that the multi-segment method proposed in this paper may be one of good methods which improve the better feelings of 3D depth in stereo image.

关键词： Pinhole segmented image Multi-segment Disparity CMOS camera module

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Multimodal image Fusion for Enhanced Brain Tumor Detection using Advanced Machine Learning Techniques 2

Multimodal Image Fusion for Enhanced Brain Tumor Detection u...

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2nd International Conference on Inventive Computing and Informatics (ICICI)

作者： Dwarakadeesh, K. Shinde, Sritej Kanagaraju, P. SRM Inst Sci & Technol Dept Computat Intelligence Chennai Tamil Nadu India

ISBN: (纸本)9798350373301;9798350373295

The use of machine learning techniques to improve the diagnosis of brain tumours has gained popularity in recent years. In particular, multimodal image fusion has emerged as a promising approach to combining information from multiple imaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), to improve diagnostic accuracy. This research uses machine learning algorithms to present a unique multimodal image fusion method for increased brain tumour detection. The proposed method starts by preprocessing the input images, including noise reduction and image registration. Then, feature extraction is performed to extract relevant information from each modality. After that, a fusion algorithm is utilised to create a single fused image by combining the features that were derived from several modalities. Finally, a machine learning classifier is trained on the fused image to perform brain tumour classification. The proposed method holds great potential in clinical practice, providing valuable support to radiologists and assisting in the accurate diagnosis of brain tumours. Multimodal image fusion, brain tumour detection, machine learning, and advanced techniques

关键词： MachineLearning Magnetic resonance imaging Segmentation Computing Topography fusing of images segmented image

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Diagnosing skin cancer via C-means segmentation with enhanced fuzzy optimization

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IET image PROCESSING 2021年第10期15卷 2266-2280页

作者： Durgarao, Nagayalanka Sudhavani, Ghanta Acharya Nagarjuna Univ Dept ECE Guntur Andhra Pradesh India RVR & JC Coll Engn Dept ECE Guntur Andhra Pradesh India

The early detection of cancer decreases the death rate as well, but mostly the disorder symptoms are unpredictable. Numerous skin cancer detection techniques are available in the dice, yet the effectiveness remains unachieved. This paper aims to introduce a skin cancer detection technique that characterizes the nature of cancer: normal, benign or malignant. The proposed technique includes three stages like Segmentation, Feature Extraction, and Classification. Here, the Fuzzy C-means Clustering (FCM) is used to segment the given input image. Then, the features are mined from the segmented image using Local Vector Pattern (LVP) and Local Binary Pattern (LBP). Subsequently, the Fuzzy classifier is used to do the classification process that gets the extracted features (LVP+LBP) as the input. The classifier outputs the nature of the image. As the primary contribution of this work, the limits of membership functions in the Fuzzy classifier are optimally selected by a new improved Rider Optimization Algorithm (ROA) termed as Distance Oriented ROA (DOROA). The performance of the proposed DOROA model is compared over other conventional models in terms of accuracy, sensitivity, specificity, precision, Negative Predictive Value (NPV), F1-score and Matthews correlation coefficient (MCC), False positive rate (FPR), False Negative Rate (FNR), and False Discovery Rate (FDR).

关键词： pattern clustering skin cancer detection techniques improved Rider Optimization Algorithm Local Vector Pattern optimisation cancer skin skin cancer detection technique feature extraction enhanced fuzzy optimization image segmentation False Negative Rate disorder symptoms Local Binary Pattern classification process Feature Extraction fuzzy set theory medical image processing Fuzzy classifier death rate LVP+LBP image classification segmented image

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Optimised hybrid classifiers for automatic HEp-2 cell classification

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IET image PROCESSING 2020年第16期14卷 4316-4328页

作者： Chariyamparambil Chandran, Manju Victor Jose, Marianthiran Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil 629180 TN India Noorul Islam Ctr Higher Educ Dept MCA Kumaracoil 629180 TN India

This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images;the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures.

关键词： medical image processing image segmentation image classification cellular biophysics feature extraction neural nets image texture diseases image representation improved diagnosis ability huge intra-class deviations HEp-2 cell classification model input image segmented image convolutional neural network classifier segmentation process co-occurrence matrix grey-level run-length matrix classification process final classified output classification accuracy randomised update-based grey wolf optimisation algorithm optimised hybrid classifiers automatic HEp-2 cell classification human epithelial type 2 cell image classifications computer-aided diagnosis

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A SYSTEM FOR INTERPRETATION OF LINE DRAWINGS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1990年第10期12卷 978-992页

作者： KASTURI, R BOW, ST ELMASRI, W SHAH, J GATTIKER, JR MOKATE, UB NO ILLINOIS UNIV DE KALBIL 60115

A system for interpretation of images of paper-based line drawings is described. Since a typical drawing contains both text strings and graphics, an algorithm has been developed to locate and separate text strings of various font sizes, styles, and orientations. This is accomplished by applying the Hough transform to the centroids of connected components in the image. The graphics in the segmented image are processed to represent thin entities by their core-lines and thick objects by their boundaries. The core-lines and boundaries are segmented into straight line segments and curved lines. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Such a description includes the location and attributes of simple polygonal shapes, circles, and interconnecting lines, and a description of the spatial relationships and occlusions among them. Hatching and filling patterns are also identified. The performance of the system is evaluated using several test images, and the results are presented. The superiority of these algorithms in generating meaningful interpretations of graphics, compared to conventional data compression schemes, is clear from these results.","doi":"10.1109/34.58870","publicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","startPage":"978","endPage":"992","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=A+system+for+interpretation+of+line+drawings&isbn=&publicationDate=Oct.+1990&author=R.+Kasturi&ContentID=10.1109/34.58870&orderBeanReset=true&startPage=978&endPage=992&volumeNum=12&issueNum=10","displayPublicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","pdfPath":"/iel1/34/2141/***","keywords":[{"type":"IEEE Keywords","kwd":["Graphics","image segmentation","Engineering drawings","Data compression","image converters","Pattern reco

关键词： Graphics image segmentation Engineering drawings Data compression image converters Pattern recognition image recognition Text recognition Testing Shape INSPEC: Controlled Indexing knowledge based systems document image processing INSPEC: Non-Controlled Indexing filling patterns document image processing IKBS knowledge based system hatching paper-based line drawings text strings graphics Hough transform segmented image thin entities core-lines thick objects boundaries minimum redundancy loops

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