Computer aided Diabetes Diagnosis using Textural Features of Saliva Crystallogram Images

2021 ◽  
Author(s):  
Srideep Maity ◽  
Manjunatha Mahadevappa ◽  
Gorachand Dutta ◽  
Jyotirmoy Chatterjee
Keyword(s):  
Textural Features ◽  
Diabetes Diagnosis ◽  
Computer Aided

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.

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.

scholarly journals Vessel Extraction of Conjunctival Images Using LBPs and ANFIS

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Seyed Mohsen Zabihi ◽  
Hamid Reza Pourreza ◽  
Touka Banaee
Keyword(s):  
Side Effects ◽  
Medical Imaging ◽  
Textural Features ◽  
Diabetes Diagnosis ◽  
Vessel Extraction ◽  
In The Beginning

The main goal of medical imaging applications is to diagnose some diseases, try to prevent the progression of them, and actually cure the patients. The number of people that suffer from diabetes is growing very fast these recent years in many countries and it is needed to diagnose this disease in the beginning to prevent the subsequent side effects like blindness and so on. One of the first ways to detect this disease is analysis of vessels in some parts of the eye such as retina and conjunctiva. Some studies have been done on effects of vessel changes of conjunctiva in diabetes diagnosis and it is proved that conjunctival vessel extraction and analysis is a good way for this purpose. In this paper, we proposed a method to detect and extract the vessels of conjunctiva automatically. It is the first stage of the process of diabetes diagnosis. We first extract some textural features from each pixel of the conjunctiva image using LBP and then classify each pixel to vessels or nonvessels according to the features vector based on a supervised classifier, ANFIS. We tested the proposed algorithm on 40 conjunctival images to show the performance and efficiency of our method.

Study of Brain Computer Aided Diagnostic System Based on CT Image

2014 ◽  
Vol 530-531 ◽  
pp. 297-300
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Diagnostic System ◽  
Textural Features ◽  
Ct Image ◽  
Grey Level ◽  
Brain Ct ◽  
Computer Aided ◽  
Image Test ◽  
Diagnostic Technology ◽  
Neural Network Algorithm ◽  
Abnormal Brain

Brain computer aided diagnostic system based on CT image has been widely applied for medical clinical field, which studies image preprocessing, feature extraction and image classification diagnosis based on digital image processing technology. This paper presents system design and realization of aided diagnostic technology for brain CT image. The dynamic grey level range of CT image is extended by adopting segmental linear stretching method at first. Then textural features of CT image are extracted based on GLCM (grey level concurrence matrix). BP neural network algorithm is used to design a classifier for textural features vector of CT image at last, which identifies normal and abnormal brain CT image. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

FUNGIFORM PAPILLAE HYPERPLASIA (FPH) IDENTIFICATION BY TONGUE TEXTURE ANALYSIS

2009 ◽  
Vol 09 (03) ◽  
pp. 479-494 ◽  
Author(s):  
BO HUANG ◽  
NAIMIN LI
Keyword(s):  
Gabor Filter ◽  
Region Of Interest ◽  
Image Database ◽  
Textural Features ◽  
Data Sets ◽  
Pathological Features ◽  
Fungiform Papillae ◽  
Linear Discriminant ◽  
Computer Aided ◽  
2D Gabor Filter

Computerized tongue diagnosis can make use of a number of pathological features of the tongue. To date, there have been few computerized applications that focus on the very commonly used and distinctive diagnostic and textural features of the tongue, Fungiform Papillae Hyperplasia (FPH). In this paper, we propose a computer-aided system for identifying the presence or absence of FPH. We first define and partition a region of interest (ROI) for texture acquisition. After preprocessing for detection and removal of reflective points, a set of 2D Gabor filter banks is used to extract and represent textural features. Then, we apply the Linear Discriminant Analysis (LDA) to identify the data sets from the tongue image database. The experimental results reasonably demonstrate the effectiveness of the method described in this paper.

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