Computer-Aided Textural Features-Based Comparison of Segmentation Methods for Melanoma Diagnosis

2020 ◽  
pp. 81-93
Author(s):  
Khushmeen Kaur Brar ◽  
Ashima Kalra ◽  
Piyush Samant
Keyword(s):  
Textural Features ◽  
Melanoma Diagnosis ◽  
Segmentation Methods ◽  
Computer Aided

scholarly journals Modern Techniques for Computer-Aided Melanoma Diagnosis

10.5772/23388 ◽  
2011 ◽  
Author(s):  
Maciej Ogorzaek ◽  
Leszek Nowak ◽  
Grzegorz Surowka ◽  
Ana Alekseenko
Keyword(s):  
Melanoma Diagnosis ◽  
Computer Aided

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

2017 ◽  
Vol 10 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Khadidja Belattar ◽  
Sihem Mostefai ◽  
Amer Draa
Keyword(s):  
Skin Lesion ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Query Image ◽  
Melanoma Diagnosis ◽  
Computer Aided ◽  
Histogram Intersection ◽  
Dermoscopic Image ◽  
Image Query ◽  
Aided 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.

scholarly journals Computer-Aided Detection of Hyperacute Stroke Based on Relative Radiomic Patterns in Computed Tomography

2019 ◽  
Vol 9 (8) ◽  
pp. 1668 ◽  
Author(s):  
Chung-Ming Lo ◽  
Peng-Hsiang Hung ◽  
Kevin Li-Chun Hsieh
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Early Recognition ◽  
Area Under The Curve ◽  
Textural Features ◽  
Local Contrast ◽  
Cad System ◽  
Learning Classifier ◽  
Computer Aided ◽  
Two Sides

Ischemic stroke is one of the leading causes of disability and death. To achieve timely assessments, a computer-aided diagnosis (CAD) system was proposed to perform early recognition of hyperacute ischemic stroke based on non-contrast computed tomography (NCCT). In total, 26 patients with hyperacute ischemic stroke (with onset <6 h previous) and 56 normal controls composed the image database. For each NCCT slice, textural features were extracted from Ranklet-transformed images which had enhanced local contrast. Textural differences between the two sides of an image were calculated and combined in a machine learning classifier to detect stroke areas. The proposed CAD system using Ranklet features achieved significantly higher accuracy (81% vs. 71%), specificity (90% vs. 79%), and area under the curve (Az) (0.81 vs. 0.73) than conventional textural features. Diagnostic suggestions provided by the CAD system are fast and promising and could be useful in the pipeline of hyperacute ischemic stroke assessments.

Computer-aided melanoma diagnosis

2002 ◽  
Vol 20 (4) ◽  
pp. 735-747 ◽  
Author(s):  
Marek Elbaum
Keyword(s):  
Melanoma Diagnosis ◽  
Computer Aided

scholarly journals Computer Aided Diagnosis of Melanoma Skin Cancer using Clinical Photographic Images

2013 ◽  
Vol 10 (8) ◽  
pp. 1922-1929 ◽  
Author(s):  
Mai S. Mabrouk ◽  
Mariam Sheha ◽  
Amr A. Sharawy
Keyword(s):  
Skin Cancer ◽  
Prediction Accuracy ◽  
Computer Aided Diagnosis ◽  
Clinical Images ◽  
Diagnosis System ◽  
Photographic Images ◽  
Melanoma Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Melanoma Skin

Melanoma is considered as one of the most malignant, metastatic and dangerous form of skin cancer that may cause death. The curability and survival of this type of skin cancer depends directly on the diagnosis and removal of melanoma in its early stages. The accuracy of the clinical diagnosis of melanoma with the unaided eye is only about 60% depending only on the knowledge and experience that each doctor has accumulated. The need to the Computer-Aided Diagnosis system (CAD) is increased to be used as a non-invasive supporting tool for physicians as a second opinion to increase the accuracy of detection, as well contributing information about the essential optical characteristics for identifying them. The ultimate aim of this research is to design an automated low cost computer aided diagnosis system of melanoma skin cancer to increase system flexibility, availability. Also, investigate to what extent melanoma diagnosis can be impacted using clinical photographic images instead of using dermoscopic ones, regarding that both are applied upon the same automatic diagnosis system. Texture features was extracted from 140 pigmented skin lesion (PSL) based on Grey level Co-occurrence matrix (GLCM), effective features are selected by fisher score ranking and then classified using Artificial Neural Network (ANN), the whole system is processed through an interactive Graphical User Interface (GUI) to achieve simplicity. Results revealed the high performance of the proposed CAD system to discriminate melanoma from melanocytic skin tumors using texture analysis when applied on clinical photographic images with prediction accuracy of 100 % for the training phase and 91 % for the testing phase. Also, results indicated that using this type of images provides high prediction accuracy for melanoma diagnosis relevant to dermoscopic images considering that photographic clinical images are acquired using less expensive consumer which exhibit a certain degree of accuracy toward the edges of our field of view.

scholarly journals Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Delia Mitrea ◽  
Paulina Mitrea ◽  
Sergiu Nedevschi ◽  
Radu Badea ◽  
Monica Lupsor ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
Classification Performance ◽  
Benign Tumors ◽  
Textural Features ◽  
Noninvasive Diagnosis ◽  
Ultrasound Images ◽  
Grey Level ◽  
Abdominal Tumor ◽  
Tumor Characterization ◽  
Computer Aided

The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue.

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