scholarly journals Extraction of Texture Features using GLCM and Shape Features using Connected Regions

2016 ◽  
Vol 8 (6) ◽  
pp. 2926-2930 ◽  
Author(s):  
Shijin Kumar P.S ◽  
Dharun V.S
Keyword(s):  
Texture Features ◽  
Shape Features

scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

2018 ◽  
pp. 162-183
Author(s):  
Vanika Singhal ◽  
Preety Singh
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Texture Features ◽  
Automatic Detection ◽  
Support Vector ◽  
Shape Features ◽  
Geometrical Properties ◽  
Correlation Based Feature Selection ◽  
Human Interpretation

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

The Art of Shaping Materials

2019 ◽  
Vol 8 (3-4) ◽  
pp. 407-433 ◽  
Author(s):  
Filipp Schmidt
Keyword(s):  
Visual Perception ◽  
Human Skin ◽  
Material Properties ◽  
Vision Research ◽  
Texture Features ◽  
Viscous Fluids ◽  
Shape Features ◽  
Visual Artists ◽  
Material Perception ◽  
Single Block

Material perception — the visual perception of stuff — is an emerging field in vision research. We recognize materials from shape, color and texture features. This paper is a selective review and discussion of how artists have been using shape features to evoke vivid impressions of specific materials and material properties. A number of examples are presented in which visual artists render materials or their transformations, such as soft human skin, runny or viscous fluids, or wrinkled cloth. They achieve this by expressing the telltale shape features of these materials and transformations, often by carving them from a single block of marble or wood. Vision research has just begun to investigate these very shape features, making material perception a prime example of how art can inform science.

A New Object Recognition Approach Using Combination of Texture, Color and Shape Features

2015 ◽  
Vol 761 ◽  
pp. 111-115
Author(s):  
Abdul Kadir ◽  
K.A.A. Aziz ◽  
Irianto
Keyword(s):  
Texture Features ◽  
Shape Features ◽  
Accuracy Rate ◽  
New Approach ◽  
Orthogonal Moments ◽  
Krawtchouk Moments ◽  
Image Histogram ◽  
Color Features ◽  
Tchebichef Moments ◽  
Skewness And Kurtosis

This paper reports a new approach for recognizing objects by using combination of texture, color and shape features. Texture features were generated by applying statistical calculation on the image histogram. Color features were computed by using mean, standard deviation, skewness and kurtosis. Shape features were generated using combination of Shen features and basic shapes such as eccentricity and dispersion. The total features were used much less compared to approaches that involve orthogonal moments such as Krawtchouk moments, Zernike moments, or Tchebichef moments. Testing was done by using a dataset that contains 53 kinds of objects. All objects contained in the dataset were various things that can be found in supermarkets or produced by manufacturing. The result shows that the system gave 98.11% of accuracy rate.

Indexing of Image Features Using Quadtree

2016 ◽  
pp. 185-203
Author(s):  
N. Puviarasan ◽  
R. Bhavani
Keyword(s):  
Extraction Method ◽  
Dimensional Space ◽  
Texture Features ◽  
Image Features ◽  
High Dimensional ◽  
Shape Features ◽  
Indexing Methods ◽  
Feature Extraction Method ◽  
Indexing Method ◽  
Color Texture

In Content based image retrieval (CBIR) applications, the idea of indexing is mapping the extracted descriptors from images into a high-dimensional space. In this paper, visual features like color, texture and shape are considered. The color features are extracted using color coherence vector (CCV), texture features are obtained from Segmentation based Fractal Texture Analysis (SFTA). The shape features of an image are extracted using the Fourier Descriptors (FD) which is the contour based feature extraction method. All features of an image are then combined. After combining the color, texture and shape features using appropriate weights, the quadtree is used for indexing the images. It is experimentally found that the proposed indexing method using quadtree gives better performance than the other existing indexing methods.

Approach of Using Texture and Shape for Image Retrieval

2013 ◽  
pp. 30-45
Author(s):  
Gang Zhang ◽  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Feature Integration ◽  
Shape Features ◽  
Important Research ◽  
Information Redundancy ◽  
Single Feature ◽  
Descriptor Approach

Feature integration is one of important research contents in content-based image retrieval. Single feature extraction and description is foundation of the feature integration. Features from a single feature extraction approach are a single feature or composite features, whether integration features are more discriminative than them or not. An approach of integrating shape and texture features was presented and used to study these problems. Gabor wavelet transform with minimum information redundancy was used to extract texture features, which would be used for feature analyses. Fourier descriptor approach with brightness was used to extract shape features. Then both features were integrated in parallel by weights. Comparisons were carried out among the integration features, the texture features, and the shape features, so that discrimination of the integration features can be testified.

scholarly journals PSO Optimized Log Gabor QBIC System

2019 ◽  
Vol 8 (11) ◽  
pp. 1650-1654
Keyword(s):  
Retrieval System ◽  
Texture Features ◽  
Research Area ◽  
Subsequent Stage ◽  
Shape Features ◽  
Swarm Optimization ◽  
Retrieval Rate ◽  
Color Features ◽  
Fitting Algorithm ◽  
Log Gabor

In modern years, there is substantially technical progression in research area pertaining to image retrieval, in specific Query By Image Content (QBIC) system. It has turned out to be essential to deliver adept and effective method to retrieve images from the gigantic collections of images utilized in heterogeneous applications. In this paper, a hybrid QBIC retrieval system known to be PSO optimized Log Gabor QBIC system that retrieves color features, texture features and shape features of the images in three consecutive stages has been developed. In the proposed system, color features are retrieved by means of color histogram in the first stage. In subsequent stage, the texture features are extracted by tuning Log Gabor filters using Particle Swarm Optimization(PSO). Lastly, shape features are retrieved by polygonal fitting algorithm. The recommended method displays enhanced retrieval rate in terms of mean recall and mean precision when compared to the prevailing standard systems.

scholarly journals A Facial Expression Recognition Model Based on Texture and Shape Features

2020 ◽  
Vol 37 (4) ◽  
pp. 627-632
Author(s):  
Aihua Li ◽  
Lei An ◽  
Zihui Che
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Shape Features ◽  
The Face ◽  
Facial Expression Classification

With the development of computer vision, facial expression recognition has become a research hotspot. To further improve the accuracy of facial expression recognition, this paper probes deep into image segmentation, feature extraction, and facial expression classification. Firstly, the convolution neural network (CNN) was adopted to accurately separate the salient regions from the face image. Next, the Gaussian Markov random field (GMRF) model was improved to enhance the ability of texture features to represent image information, and a novel feature extraction algorithm called specific angle abundance entropy (SAAE) was designed to improve the representation ability of shape features. After that, the texture features were combined with shape features, and trained and classified by the support vector machine (SVM) classifier. Finally, the proposed method was compared with common methods of facial expression recognition on a standard facial expression database. The results show that our method can greatly improve the accuracy of facial expression recognition.

scholarly journals Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

2021 ◽  
pp. 435-439
Author(s):  
Rajani Kumari ◽  
C. Thanuja ◽  
K. Sai Thanvi ◽  
K. Lakshmi ◽  
U. Lavanya
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
Lung Nodules ◽  
Shape Features ◽  
Rule Based ◽  
Occurrence Matrix

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

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