scholarly journals A new range-based image texture measure with application to mammogram image analysis

2000 ◽  
Author(s):  
R. Chandrasekhar
Keyword(s):  
Image Analysis ◽  
Image Texture ◽  
Mammogram Image

scholarly journals Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

2015 ◽  
Vol 173 (4) ◽  
pp. M39-M44 ◽  
Author(s):  
R P Kosilek ◽  
R Frohner ◽  
R P Würtz ◽  
C M Berr ◽  
J Schopohl ◽  
...  
Keyword(s):  
Image Analysis ◽  
Binary Classification ◽  
Genetic Disorders ◽  
Disease Onset ◽  
Image Texture ◽  
Medical Setting ◽  
Future Perspectives ◽  
Genetic Syndromes ◽  
Pilot Studies ◽  
Ongoing Research

Cushing's syndrome (CS) and acromegaly are endocrine diseases that are currently diagnosed with a delay of several years from disease onset. Novel diagnostic approaches and increased awareness among physicians are needed. Face classification technology has recently been introduced as a promising diagnostic tool for CS and acromegaly in pilot studies. It has also been used to classify various genetic syndromes using regular facial photographs. The authors provide a basic explanation of the technology, review available literature regarding its use in a medical setting, and discuss possible future developments. The method the authors have employed in previous studies uses standardized frontal and profile facial photographs for classification. Image analysis is based on applying mathematical functions evaluating geometry and image texture to a grid of nodes semi-automatically placed on relevant facial structures, yielding a binary classification result. Ongoing research focuses on improving diagnostic algorithms of this method and bringing it closer to clinical use. Regarding future perspectives, the authors propose an online interface that facilitates submission of patient data for analysis and retrieval of results as a possible model for clinical application.

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

2013 ◽  
Vol 25 (03) ◽  
pp. 1350029 ◽  
Author(s):  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Mammogram Image ◽  
Background Contrast

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

Prediction of texture in different beef cuts applying image analysis technique

2018 ◽  
Vol 120 (8) ◽  
pp. 1929-1940
Author(s):  
Facundo Pieniazek ◽  
Agustina Roa Andino ◽  
Valeria Messina
Keyword(s):  
Image Analysis ◽  
Linear Trend ◽  
Texture Features ◽  
Image Texture ◽  
Content Type ◽  
Analysis Techniques ◽  
Texture Parameters ◽  
Freeze Dried ◽  
Instrumental Texture ◽  
Image Analysis Techniques

Purpose Measuring texture parameters are time consuming and expensive; it is necessary to develop an efficient and rapid method to evaluate them. Image analysis can be a useful tool. The purpose of this paper is to predict texture parameters in different beef cuts applying image analysis techniques. Design/methodology/approach Samples were analyzed by scanning electron microscopy. Texture parameters were analyzed by instrumental, image analysis techniques and by Warner–Bratzler shear force. Findings Significant differences (p<0.05) were obtained for image and instrumental texture features. Higher amount of porous were observed in freeze dried samples of beef cuts from Gluteus Medius and semintendinosus muscles. A linear trend with a linear correlation was applied for instrumental and image texture. High correlations were found between image and instrumental texture features. Instrumental parameters showed a positive correlation with image texture feature. Originality/value This research suggests that the addition of image texture features improves the accuracy to predict texture parameter. The prediction of quality parameters can be performed easily with a computer by recognizing attributes within an image.

scholarly journals EVALUATION OF THE RISK OF STROKE WITH CONFIDENCE PREDICTIONS BASED ON ULTRASOUND CAROTID IMAGE ANALYSIS

2012 ◽  
Vol 21 (04) ◽  
pp. 1240016 ◽  
Author(s):  
ANTONIS LAMBROU ◽  
HARRIS PAPADOPOULOS ◽  
EFTHYVOULOS KYRIACOU ◽  
CONSTANTINOS S. PATTICHIS ◽  
MARIOS S. PATTICHIS ◽  
...  
Keyword(s):  
Image Analysis ◽  
Stroke Risk ◽  
Machine Learning Algorithms ◽  
Image Texture ◽  
Support Vector ◽  
Ultrasound Images ◽  
Image Texture Analysis ◽  
Confidence Measures ◽  
Conformal Predictors ◽  
Morphological Image Analysis

Conformal Predictors (CPs) are Machine Learning algorithms that can provide reliable confidence measures to their predictions. In this work, we make use of the Conformal Prediction framework for the assessment of stroke risk based on ultrasound images of atherosclerotic carotid plaques. For this application, images were recorded from 137 asymptomatic and 137 symptomatic plaques (symptoms are Stroke, Transient Ischaemic Attack (TIA), and Amaurosis Fugax (AF)). Two feature sets were extracted from the plaques; the first based on morphological image analysis and the second based on image texture analysis. Both sets were used in order to evaluate the performance of CPs on this problem. Four CPs were constructed using four popular classification methods, namely Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes Classification (NBC), and k -Nearest Neighbours. The results given by all CPs demonstrate the reliability and importance of the obtained confidence measures on the problem of stroke risk assessment.

Classification of Mammogram Abnormalities Using Legendre Moments

2020 ◽  
pp. 2150010
Author(s):  
Rodrigo Dalvit C. Silva ◽  
Thomas R. Jenkyn
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Accuracy Rate ◽  
Feature Extractor ◽  
Analysis Society ◽  
Mammogram Image ◽  
Legendre Moments

In this paper, the issue of classifying mammogram abnormalities using images from an mammogram image analysis society (MIAS) database is discussed. We compare a feature extractor based on Legendre moments (LMs) with six other feature extractors. To determine the best feature extractor, the performance of each was compared in terms of classification accuracy rate and extraction time using a [Formula: see text]-nearest neighbors ([Formula: see text]-NN) classifier. This study shows that feature extraction using LMs performed best with an accuracy rate over 84% and requiring relatively little time for feature extraction, on average only 1[Formula: see text]s.

scholarly journals Image analysis of weaverbird nests reveals signature weave textures

2015 ◽  
Vol 2 (6) ◽  
pp. 150074 ◽  
Author(s):  
Ida E. Bailey ◽  
André Backes ◽  
Patrick T. Walsh ◽  
Kate V. Morgan ◽  
Simone L. Meddle ◽  
...  
Keyword(s):  
Image Analysis ◽  
Texture Analysis ◽  
Individual Variation ◽  
Texture Classification ◽  
Image Texture ◽  
Analysis Approach ◽  
Computer Aided ◽  
The Individual

In nature, many animals build structures that can be readily measured at the scale of their gross morphology (e.g. length, volume and weight). Capturing individuality as can be done with the structures designed and built by human architects or artists, however, is more challenging. Here, we tested whether computer-aided image texture classification approaches can be used to describe textural variation in the nests of weaverbirds ( Ploceus species) in order to attribute nests to the individual weaverbird that built them. We found that a computer-aided texture analysis approach does allow the assignment of a signature to weaverbirds' nests. We suggest that this approach will be a useful tool with which to examine individual variation across a range of animal constructions, not just for nests.

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