scholarly journals ROLE OF MACHINE VISION FOR IDENTIFICATION OF KIDNEY STONES USING MULTI FEATURES ANALYSIS

2021 ◽  
Vol 5 (3) ◽  
pp. 1-14
Author(s):  
Salman Qadri
Keyword(s):  
Random Forest ◽  
Machine Vision ◽  
Optimization Technique ◽  
Texture Feature ◽  
Region Of Interest ◽  
Texture Features ◽  
Feature Space ◽  
Data Set ◽  
Feature Optimization ◽  
Frame Work

The purpose of this study is to highlight the significance of machine vision for the Classification of kidney stone identification. A novel optimized fused texture features frame work was designed to identify the stones in kidney.  A fused 234 texture feature namely (GLCM, RLM and Histogram) feature set was acquired by each region of interest (ROI). It was observed that on each image 8 ROI’s of sizes (16x16, 20x20 and 22x22) were taken. It was difficult to handle a large feature space 280800 (1200x234). Now to overcome this data handling issue we have applied feature optimization technique namely POE+ACC and acquired 30 most optimized features set for each ROI. The optimized fused features data set 3600(1200x30) was used to four machine vision Classifiers that is Random Forest, MLP, j48 and Naïve Bayes. Finally, it was observed that Random Forest provides best results of 90% accuracy on ROI 22x22 among the above discussed deployed Classifiers

scholarly journals Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

2018 ◽  
Vol 7 (2.20) ◽  
pp. 291 ◽  
Author(s):  
B Saroja ◽  
A Selwin Mich Priyadharson
Keyword(s):  
Colon Cancer ◽  
Texture Feature ◽  
Image Data ◽  
Texture Features ◽  
Gray Level ◽  
Colon Tissue ◽  
Data Set ◽  
Histopathological Image ◽  
Cancer Biopsy ◽  
Cancer Tissues

Colon or Bowel or Colorectal Cancer (CRC) is commonly determined by diagnosing a sample of colon tissue and further analysed by medical imaging. The colon tissue classification method count on specific changes between texture features extracted from benign and malignant regions. The variations in the image acquisition methods effects the colon tissue analysis. In this paper, an Upgraded Spatial Gray Level Dependence Matrices (U-SGLDM) is emphasized to extract textural features. The licensed image set of all applicable types of tissues within colon cancer are used for experimentation. Several texture feature sets are extracted to show the significant differences among the eight colon cancer biopsy images in the image data set. The fractal dimension-Hurst Coefficient is added to U-SGLDM for long range assessment. The Prominence of the analysis evoked in the representation of histopathological image structure over longer periods.  

scholarly journals Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Salman Qadri ◽  
Dost Muhammad Khan ◽  
Syed Furqan Qadri ◽  
Abdul Razzaq ◽  
Nazir Ahmad ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Machine Vision ◽  
Data Fusion ◽  
Spectral Feature ◽  
Texture Feature ◽  
Texture Features ◽  
Features Selection ◽  
Land Use Land Cover ◽  
Multispectral Data

Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing. A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly. Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texture-feature dataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually both datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.

scholarly journals A Random Forest and Current Fault Texture Feature–Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Kou ◽  
Xiao-dong Gong ◽  
Yi Zheng ◽  
Xiu-hui Ni ◽  
Yang Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Texture Feature ◽  
Texture Features ◽  
Current Sensor ◽  
Stable Operation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Three Phase ◽  
Feature Based

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature–based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

Breast Cancer Detection Using Random Forest Classifier

2021 ◽  
pp. 85-98
Author(s):  
Pavithra Suchindran ◽  
Vanithamani R. ◽  
Judith Justin
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Texture Features ◽  
Speckle Noise ◽  
Random Forest Classifier ◽  
Breast Cancer Detection ◽  
K Nearest Neighbors ◽  
Cad System

Breast cancer is the second most prevalent type of cancer among women. Breast ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using speckle reducing anisotropic diffusion (SRAD) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are used in this work. The texture features are extracted and fed to a classifier to categorize the images as normal, benign, and malignant. In this work, three classifiers, namely k-nearest neighbors (KNN) algorithm, decision tree algorithm, and random forest classifier, are used and the performance is compared based on the accuracy of classification.

A COMPUTER-AIDED SYSTEM FOR MASS DETECTION AND CLASSIFICATION IN DIGITIZED MAMMOGRAMS

2005 ◽  
Vol 17 (05) ◽  
pp. 215-228 ◽  
Author(s):  
SHENG-CHIH YANG ◽  
CHUIN-MU WANG ◽  
YI-NUNG CHUNG ◽  
GIU-CHENG HSU ◽  
SAN-KAN LEE ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Diagnostic System ◽  
Region Of Interest ◽  
Texture Features ◽  
Computer Assisted ◽  
Mass Detection ◽  
Difference Matrix ◽  
Data Set ◽  
System A ◽  
Mass Classification

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

Image classification method of cashmere and wool based on the multi-feature selection and random forest method

2021 ◽  
pp. 004051752110460
Author(s):  
Yaolin Zhu ◽  
Jiameng Duan ◽  
Yunhong Li ◽  
Tong Wu
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Textile Industry ◽  
Texture Feature ◽  
Principal Component ◽  
Weight Coefficient ◽  
Data Set ◽  
Random Forest Method ◽  
Independent Features ◽  
Occurrence Matrix

Cashmere and wool play an important role in the wool industry and textile industry, and suitable features are the key to identifying them. To obtain effective features and improve the accuracy of cashmere and wool classification, the multi-feature selection and random forest method is used to express in this article. Firstly, the gray-gradient co-occurrence matrix model is used for texture feature extraction to construct the original high-dimensional feature data set; secondly, considering that the original feature data set contains a large number of invalid and redundant features, the feature selection algorithm combining correlation analysis and principal component analysis–weight coefficient evaluation is used to obtain important features, independent features, and principal component sensitive features to complement each other; last but not least, the optimized random forest model analyzes the results. The results show that the combination of multi-feature selection subsets and random forest makes the classification accuracy of cashmere and wool more reliable, and the accuracy fluctuates around 90%.

scholarly journals Texture Feature Extraction of Lumbar Spine Trabecular Bone Radiograph Image using Laplacian of Gaussian Filter with KNN Classification to Diagnose Osteoporosis

2021 ◽  
Vol 2070 (1) ◽  
pp. 012137
Author(s):  
Kavita Avinash Patil ◽  
K V Mahendra Prashanth ◽  
A Ramalingaiah
Keyword(s):  
Lumbar Spine ◽  
Trabecular Bone ◽  
Bone Fractures ◽  
Directional Derivative ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Gaussian Filter ◽  
Data Set ◽  
Laplacian Of Gaussian Filter

Abstract The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.

scholarly journals Milk Somatic Cells Recognition based on Gray-Scale Difference Statistics

2018 ◽  
Vol 173 ◽  
pp. 03065
Author(s):  
Xiaoli Zhang ◽  
Heru Xue ◽  
Gao Xiaojing
Keyword(s):  
Random Forest ◽  
Somatic Cells ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Scale ◽  
Difference Matrix ◽  
Random Forest Classification ◽  
Feature Parameters ◽  
Milk Somatic Cells ◽  
Scale Difference

In order to solve the problems in recognition of milk somatic cells and improve the recognition efficiency and accuracy, we put forward a classification algorithm based on the gray-Scale difference Statistics(GLDS). First, the algorithm extracts the gray difference matrix of the image, and from the matrix we can calculate the texture features of cell images. Then uses the K-means algorithm to segment the cells, extracts morphological features from the nucleus. We extract the 8 morphological feature parameters, 4 texture feature parameters, and use the K-fold cross-validation and random forest classification(RF) to classify samples. Experimental results show that the correct rate is 95.36% when using the random forest classifier.

scholarly journals The histogram of partitioned localized image textures

2017 ◽  
Author(s):  
◽  
Eric B. Brewster
Keyword(s):  
Object Detection ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Directional Pattern ◽  
Bag Of Words ◽  
Data Set ◽  
Pixel Location ◽  
Local Direction ◽  
Robustness To Noise

In the field of machine learning and pattern recognition, texture has been a prominent area of research. Humans are uniquely equipped to distinguish texture; however, computers are more equipped to automate the process. Computers accomplish this by taking images and extracting meaningful features that describe their texture. Some of these features are the Haralick texture features, local binary pattern (LBP), and the local direction pattern (LDP). Using the local directional pattern as an example, we propose a new texture feature called the histogram of partitioned localized image textures (HoPLIT). This feature utilizes a set of filters, not necessarily directional, and generates filter response vectors at every pixel location. These response vectors can be thought of as words in a document, which causes one to think of the bag-of-words model. Using the bag-of-words model, a codebook is created by partitioning a subset of response vectors from the entire data set. The partitions are represented by their mean texture and thus a word in the codebook. The mean textures now represent the keywords within the document, i.e. image. A histogram descriptor for an image is the frequency of pixels that belong to each partition. This feature is applied to a texture classification and segmentation problem as well as object detection. Within each problem domain, the HoPLIT feature is compared to the Haralick texture features, LBP, and LDP. The HoPLIT feature does very well classifying texture as well as segmenting large texture mosaics. HoPLIT also shows a surprising robustness to noise. Object detection proves to be slightly more difficult than texture classification for HoPLIT. However, it continues to outperform LBP and LDP.

scholarly journals Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm

2021 ◽  
Vol 13 (23) ◽  
pp. 4762
Author(s):  
Panpan Wei ◽  
Weiwei Zhu ◽  
Yifan Zhao ◽  
Peng Fang ◽  
Xiwang Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Learning Algorithm ◽  
Animal Husbandry ◽  
Feature Space ◽  
Google Earth ◽  
Random Forest Classification ◽  
Topographic Features ◽  
Forest Classification ◽  
Feature Optimization ◽  
Land Types

Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.

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