Handling of Feature Space Complexity for Texture Analysis in Medical Images

2017 ◽  
pp. 163-191
Author(s):  
Yang Song ◽  
Weidong Cai
Keyword(s):  
Texture Analysis ◽  
Medical Images ◽  
Feature Space ◽  
Space Complexity

scholarly journals Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

2020 ◽  
Vol 7 (4) ◽  
pp. 79-86
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Image Retrieval ◽  
Texture Analysis ◽  
Medical Image ◽  
Local Binary Pattern ◽  
Medical Images ◽  
Texture Classification ◽  
Medical Image Retrieval ◽  
Local Vector ◽  
Local Derivative ◽  
Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

Texture analysis of medical images

2004 ◽  
Vol 59 (12) ◽  
pp. 1061-1069 ◽  
Author(s):  
G. Castellano ◽  
L. Bonilha ◽  
L.M. Li ◽  
F. Cendes
Keyword(s):  
Texture Analysis ◽  
Medical Images

scholarly journals An Intelligent cluster-based segmentation using Wavelet Features:Application to Medical Images

2010 ◽  
Author(s):  
Thavavel V ◽  
JafferBasha J
Keyword(s):  
Medical Images ◽  
Automatic Segmentation ◽  
A Priori ◽  
User Interaction ◽  
Region Of Interest ◽  
Feature Space ◽  
Discrete Wavelet ◽  
Wavelet Frames ◽  
Translation Invariant ◽  
Spherical Coordinate

Segmentation forms the onset for image analysis especially for medical images, making any abnormalities in tissues distinctly visible. Possible application includes the detection of tumor boundary in SPECT, MRI or electron MRI (EMRI). Nevertheless, tumors being heterogeneous pose a great problem when automatic segmentation is attempted to accurately detect the region of interest (ROI). Consequently, it is a challenging task to design an automatic segmentation algorithm without the incorporation of ‘a priori’ knowledge of an organ being imaged. To meet this challenge, here we propose an intelligence-based approach integrating evolutionary k-means algorithm within multi-resolution framework for feature segmentation with higher accuracy and lower user interaction cost. The approach provides several advantages. First, spherical coordinate transform (SCT) is applied on original RGB data for the identification of variegated coloring as well as for significant computational overhead reduction. Second the translation invariant property of the discrete wavelet frames (DWF) is exploited to define the features, color and texture using chromaticity of LL band and luminance of LH and HL band respectively. Finally, the genetic algorithm based K-means (GKA), which has the ability to learn intelligently the distribution of different tissue types without any prior knowledge, is adopted to cluster the feature space with optimized cluster centers. Experimental results of proposed algorithm using multi-modality images such as MRI, SPECT, and EMRI are presented and analyzed in terms of error measures to verify its effectiveness and feasibility for medical applications.

Texture Analysis for Identifying Heterogeneity in Medical Images

2014 ◽  
pp. 446-453 ◽  
Author(s):  
Jakub Nalepa ◽  
Janusz Szymanek ◽  
Michael P. Hayball ◽  
Stephen J. Brown ◽  
Balaji Ganeshan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Medical Images

Application Of Texture Analysis In Echocardiography Images For Myocardial Infarction Tissue

2012 ◽  
Author(s):  
N. Agani ◽  
S. A. R. Abu–Bakar ◽  
S. H. Sheikh Salleh
Keyword(s):  
Myocardial Infarction ◽  
Texture Analysis ◽  
Mahalanobis Distance ◽  
Medical Images ◽  
Object Identification ◽  
Time Interval ◽  
Gray Level ◽  
Tissue Samples ◽  
Second Moment ◽  
Occurrence Matrix

Analisa tekstur adalah satu sifat penting untuk mengenal pasti permukaan dan objek daripada imej perubatan dan pelbagai imej lain. Penyelidikan ini telah membangunkan sebuah algoritma untuk menganalisa tekstur dengan menggunakan imej perubatan dari echocardiography untuk mengenal pasti jantung yang disyaki mengalami myocardial infarction. Di sini penggabungan daripada teknik wavelet extension transform dan teknik gray level co–occurrence matrix adalah dicadangkan. Di dalam penyelidikan ini wavelet extension transform digunakan untuk menghasilkan sebuah imej hampiran yang mempunyai resolusi yang lebih besar. Gray level co–occurrence matrix yang dihitung untuk setiap sub–band digunakan untuk mencirikan empat sifat vektor: entropy, contrast, energy (angular second moment) dan homogeneity (invers difference moment). Pengklasifikasian yang digunakan di dalam penyelidikan ini adalah pengklasifikasian Mahalanobis distance. Kaedah yang telah dicadangkan diuji dengan data klinikal dari imej echocardiography untuk 17 orang pesakit. Untuk setiap pesakit, contoh tisu diambil daripada kawasan yang disyaki infarcted dan kawasan non–infarcted (normal). Untuk setiap pesakit, 8 bingkai imej yang dipisahkan oleh sela waktu tertentu di mana 5 kawasan normal dan 5 kawasan disyaki myocardial infarction berukuran 16×16 piksel akan dianalisa. Hasil pengklasifikasian telah dicapai dengan ketepatan 91.32%. Kata kunci: Analisa tekstur, wavelet extension, co–occurrence matrix, myocardial infarction, sifat vektor Texture analysis is an important characteristic for surface and object identification from medical images and many other types of images. This research has developed an algorithm for texture analysis using medical images do trained from echocardiography in identifying heart with suspected myocardial infarction problem. A set of combination of wavelet extension transform with gray level co–occurrence matrix is proposed. In this work, wavelet extension transform is used to form an image approximation with higher resolution. The gray level co–occurrence matrices computed for each subband are used to extract four feature vectors: entropy, contrast, energy (angular second moment) and homogeneity (inverse difference moment). The classifier used in this work is the Mahalanobis distance classifier. The method is tested with clinical data from echocardiography images of 17 patients. For each patient, tissue samples are taken from suspected infarcted area as well as from non–infarcted (normal) area. For each patient, 8 frames separated by some time interval are used and for each frame, 5 normal regions and 5 suspected myocardial infarction regions of 16×16 pixel size are analyzed. The classification performance achieved 91.32% accuracy. Key words: Texture analysis, wavelet extension, co–occurrence matrix, myocardial infarction, feature vector

scholarly journals Exploiting Global Structure Information to Improve Medical Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3249
Author(s):  
Jaemoon Hwang ◽  
Sangheum Hwang
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Feature Space ◽  
Medical Image Segmentation ◽  
Global Structure ◽  
Distance Metrics ◽  
Anatomical Structures ◽  
Structure Information ◽  
Anatomical Priors ◽  
Segmentation Models

In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.

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