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An intelligent convolution based graph cut segmentation for potato leaf disease severity prediction

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 32765-32787页

作者： Rapaka, Anuj Kanmani, A. Clara Shri Vishnu Engn Coll Women Dept Comp Sci & Engn Bhimavaram 534202 Andhra Pradesh India PES Univ Dept Comp Sci & Engn Elect City Bangaluru India

Nowadays, all survival applications are digitalized by smart facilities. Hence, potato leaves disease prediction is considered for this study. Several imaging models and intelligent approaches were implemented in the past, but their segmentation is inappropriate due to the different pixel ranges in the affected region. So, the current work has implemented a novel Krill Herd-based optimized Convolution Neural Model (KHbOCNM) for the potato leaves disease specification process. Moreover, the potato leaf disease considered in this research investigation is Early Blight (EB) and Late Blight (LB). Moreover, the graph cut segmentation procedure has been implemented to gain the exact segmentation outcome of the disease-affected part. Incorporating the Krill herd function with the graph cut segmentation in the classification layer has afforded a significant outcome as improved segmentation accuracy of 96% and a 4% error rate. It is a sufficient score for segmenting the disease region accurately and better than the traditional methods. Hence, the proper segmentation yields to attain the exact severity prediction;it is considered as the major significance of the study.

关键词： graph cut segmentation Krill herd optimization Convolution neural network Early blight Light blight Severity analysis

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Statistical Significance Based graph cut segmentation for Shrinking Bias

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8th International Conference on Image Analysis and Recognition (ICIAR) / 2nd International Conference on Autonomous and Intelligent Systems (AIS)

作者： Candemir, Sema Akgul, Yusuf Sinan Gebze Inst Technol Dept Comp Engn GIT Comp Vis Lab Kocaeli Turkey

ISBN: (纸本)9783642215926;9783642215933

graph cut algorithms are very popular in image segmentation approaches. However, the detailed parts of the foreground are not segmented well in graph cut minimization. There are basically two reasons of inadequate segmentations: (i) Data - smoothness relationship of graph energy. (ii) Shrinking bias which is the bias towards shorter paths. This paper improves the foreground segmentation by integrating the statistical significance measure into the graph energy minimization. Significance measure changes the relative importance of graph edge weights for each pixel. Especially at the boundary parts, the data weights take more significance than the smoothness weights. Since the energy minimization approach takes into account the significance measure, the minimization algorithm produces better segmentations at the boundary regions. Experimental results show that the statistical significance measure makes the graph cut algorithm less prone to bias towards shorter paths and better at boundary segmentation.

关键词： graph cut segmentation Energy Minimization Shrinking Bias Statistical Significance Analysis

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Vessel Wall Detection in the Images of Intravascular Optical Coherence Tomography based on the graph cut segmentation 25

Vessel Wall Detection in the Images of Intravascular Optical...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Modanloujouybari, Hamed Ayatollahi, Ahmad Kermani, Ali Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781509059638

Intravascular Optical coherence tomography (IVOCT) is a catheter-based imaging method that employs near-infrared light to produce cross-sectional intravascular images. The size of lumen, where is known as vessel wall, is considered as the main indicator for artery clogging. The aim of this study is to investigate a new algorithm to extract lumen areas in IVOCT images which has time efficiency and high accuracy. The proposed algorithm consists of four main stages: pre-processing, feature extraction, kernel graph cut segmentation and finally the vessel wall detection and reconstruction. Generally, graph-based segmentation methods have a high precision in these images. So, we have used the kernel graph cut segmentation, which uses the piecewise model and Gaussian kernel function for the cost function. Furthermore, for initializing graph cut segmentation, we have used k-means clustering algorithm for the first frame. Since the amount of differences in the consecutive frames in IVOCT image collections is low, we have used the results of previous frame optimization as the initialization of the graph cut based segmentation of the current frame. The comparison results showed that suggested algorithm besides having a high precision (Hausdorff distance of 0.0653 mm and Mean distance of 0.0265 mm) also has a good processing speed in comparison with other methods (3.9 sec/frame).

关键词： Intravascular optical coherence tomography near infrared light vessel wall graph cut segmentation

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Integrating Atlas and graph cut Methods for Right Ventricle Blood-Pool segmentation from Cardiac Cine MRI

Integrating Atlas and Graph Cut Methods for Right Ventricle ...

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Conference on Medical Imaging - Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Dangi, Shusil Linte, Cristian A. Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Dept Biomed Engn Rochester NY 14623 USA Rochester Inst Technol 160 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

segmentation of right ventricle from cardiac MRI images can be used to build pre-operative anatomical heart models to precisely identify regions of interest during minimally invasive therapy. Furthermore, many functional parameters of right heart such as right ventricular volume, ejection fraction, myocardial mass and thickness can also be assessed from the segmented images. To obtain an accurate and computationally efficient segmentation of right ventricle from cardiac cine MRI, we propose a segmentation algorithm formulated as an energy minimization problem in a graph. Shape prior obtained by propagating label from an average atlas using affine registration is incorporated into the graph framework to overcome problems in ill-defined image regions. The optimal segmentation corresponding to the labeling with minimum energy configuration of the graph is obtained via graph-cuts and is iteratively refined to produce the final right ventricle blood pool segmentation. We quantitatively compare the segmentation results obtained from our algorithm to the provided gold-standard expert manual segmentation for 16 cine-MRI datasets available through the MICCAI 2012 Cardiac MR Right Ventricle segmentation Challenge according to several similarity metrics, including Dice coefficient, Jaccard coefficient, Hausdorff distance, and Mean absolute distance error.

关键词： image segmentation right ventricle segmentation cardiac atlas image registration graph cut segmentation cardiac cine-MRI statistical model cardiac function assessment

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Symbolic representation and classification of medical X-ray images

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SIGNAL IMAGE AND VIDEO PROCESSING 2015年第3期9卷 715-725页

作者： Rajaei, Amir Dallalzadeh, Elham Rangarajan, Lalitha Univ Mysore Dept Studies Comp Sci Mysore 570006 Karnataka India Islamic Azad Univ Dept Comp Marvdasht Branch Marvdasht Iran

In this paper, we propose a symbolic approach for classification of medical X-ray images. graph cut segmentation is applied to segment the body part of medical X-ray images. A complete directed graph is constructed using the centroid points in the boundary image of the segmented body part image. The complete directed graph is in turn used to extract features of distance and orientation. Further, the boundaries of segmented images are represented by its skeleton end points. Shape features are then extracted from the represented skeleton end points. To assimilate feature variations, we propose to symbolically represent the extracted features of each class in the form of interval valued features. Based on the proposed symbolic representation, symbolic classifier is then used for classification of medical X-ray images. Experimental results reveal the efficiency of our proposed symbolic classification model.

关键词： Medical X-ray image classification graph cut segmentation Centroid points Skeleton end points Distance features Orientation features Shape features Interval valued features Symbolic representation Symbolic classifier

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Change detection by probabilistic segmentation from monocular view

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MACHINE VISION AND APPLICATIONS 2014年第5期25卷 1175-1195页

作者： Hernandez-Lopez, Francisco J. Rivera, Mariano Ctr Invest Matemat AC Guanajuato Gto 36240 Mexico

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA graphics Processing Unit in order to fulfill real-time execution requirements.

关键词： Change detection QMMF segmentation graph cut segmentation Background likelihood Illumination changes Shadow detection Camouflage Background maintenance GPU Programming CUDA

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segmentation of the Blood Vessels and Optic Disk in Retinal Images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2014年第6期18卷 1874-1886页

作者： Salazar-Gonzalez, Ana Kaba, Djibril Li, Yongmin Liu, Xiaohui Brunel Univ Dept Comp Sci London UB8 3PH England Access IS Reading RG6 1AZ Berks England

Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disk in the fundus retinal images. The method could be used to support nonintrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disk is an important indicator for diseases like diabetic retinopathy, glaucoma, and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disk. The optic disk segmentation is performed using two alternative methods. The Markov random field (MRF) image reconstruction method segments the optic disk by removing vessels from the optic disk region, and the compensation factor method segments the optic disk using the prior local intensity knowledge of the vessels. The proposed method is tested on three public datasets, DIARETDB1, DRIVE, and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disk.

关键词： graph cut segmentation optic disk segmentation retinal images vessel segmentation

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Three-Dimensional segmentation of the Ex-Vivo Anterior Lamina Cribrosa From Second-Harmonic Imaging Microscopy

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2018年第7期65卷 1617-1629页

作者： Ram, Sundaresh Danford, Forest Howerton, Stephen Rodriguez, Jeffrey J. Vande Geest, Jonathan P. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA Cornell Univ Sch Biomed Engn Ithaca NY 14853 USA Univ Arizona Dept Aerosp & Mech Engn Tucson AZ 85721 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA Univ Arizona Grad Interdisciplinary Program Biomed Engn Tucson AZ 85721 USA Univ Pittsburgh Dept Bioengn Pittsburgh PA 15260 USA Univ Pittsburgh McGowan Inst Regenerat Med Pittsburgh PA 15260 USA

The lamina cribrosa (LC) is a connective tissue in the posterior eye with a complex mesh-like trabecular microstructure, through which all the retinal ganglion cell axons and central retinal vessels pass. Recent studies have demonstrated that changes in the structure of the LC correlate with glaucomatous damage. Thus, accurate segmentation and reconstruction of the LC is of utmost importance. This paper presents a new automated method for segmenting the microstructure of the anterior LC in the images obtained via multiphoton microscopy using a combination of ideas. In order to reduce noise, we first smooth the input image using a 4-D collaborative filtering scheme. Next, we enhance the beam-like trabecular microstructure of the LC using wavelet multiresolution analysis. The enhanced LC microstructure is then automatically extracted using a combination of histogram thresholding and graph-cut binarization. Finally, we use morphological area opening as a postprocessing step to remove the small and unconnected 3-D regions in the binarized images. The performance of the proposed method is evaluated usingmutual overlap accuracy, Tanimoto index, F-score, and Rand index. Quantitative and qualitative results show that the proposed algorithm provides improved segmentation accuracy and computational efficiency compared to the other recent algorithms.

关键词： graph cut segmentation histogram thresholding lamina cribrosa volumetric data denoising wavelet

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Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

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Computers, Materials & Continua 2022年第10期73卷 849-864页

作者： Romany F.Mansour Sara A.Althubiti Fayadh Alenezi Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka72388Saudi Arabia

Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of *** the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging *** study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)*** goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic ***,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is ***,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature ***,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification *** application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet *** experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion *** detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.

关键词： Skin lesion detection dermoscopic images machine learning deep learning graph cut segmentation EfficientNet

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Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2015年第1期2015卷 1-15页

作者： Ccacyahuillca Bejar, Hans Harley Miranda, Paulo Av Univ Sao Paulo Dept Comp Sci BR-05508090 Sao Paulo Brazil

Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help alleviate a part of this problem. In this work, we discuss how to incorporate this property in the relative fuzzy connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named oriented relative fuzzy connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show its usage to devise powerful hybrid image segmentation methods. The methods are evaluated using medical images of MRI and CT of the human brain and thoracic studies.

关键词： Relative fuzzy connectedness Image foresting transform graph cut segmentation graph search algorithms

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