scholarly journals Design and Development of an Efficient Mining Framework for Pre-Cancerous Lesion Detection in Lung using Non-Invasive CT Imaging

2019 ◽  
Vol 9 (1) ◽  
pp. 4790-4794
Keyword(s):  
Medical Information ◽  
Automatic Segmentation ◽  
Precancerous Lesion ◽  
Region Of Interest ◽  
Research Problem ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Irregular Shapes ◽  
Cancerous Lesion ◽  
Cancerous Lesions

The ability to analyze and identify meaningful patterns in clinical data must be addressed to provide a better understanding of disease. Currently existing solutions for disease diagnosis systems are costly, time consuming and prone to errors, due to the diversity of medical information sources. Lung Disease Diagnosis individual is based on medical images (Lung CTs) includes Lung segmentation, and the detection of cancerous lesions in the Lung. Segmenting the region of interest from medical imaging is a challenge, since the images are varied, complex and can contain irregular shapes with noisy values.In this context, the segmentation of the Region of Interest from Lung CT and detecting the pre-cancerous lesions is an important research problem that is receiving growing attention. Hence an efficient methodology on ACM based automatic segmentation and precancerous lesion detection is proposed.

An Efficient Coronary Disease Diagnosis System Using Dual-Phase Multi-Objective Optimization and Embedded Feature Selection

2017 ◽  
Vol 13 (3) ◽  
pp. 15-36 ◽  
Author(s):  
Priyatharshini R. ◽  
Chitrakala S.
Keyword(s):  
Feature Selection ◽  
Coronary Artery ◽  
Active Contour Model ◽  
Feature Selection Method ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Optimization Approach ◽  
Slice Thickness ◽  
Dual Phase ◽  
Scoring Method

Developments in healthcare technologies have significantly enhanced spatial resolution and improved contrast resolution, permitting analysis of additional subtle structures than formerly attainable. An approach for Automatic recognition and quantification of calcifications from arteries in computed tomography (CT) scans is developed which is a key necessity in planning the treatment of individuals with suspected coronary artery disease. First, a Dual-Phase Multi-_objective Optimization approach using an Active Contour Model-based region-growing technique is developed. Second, an embedded feature selection method is developed with an expert classifier to detect calcified objects in the segmented artery with great accuracy. Finally, the Agatston scoring method is utilized to quantify the level of coronary artery calcium plaque. Coronary CT images from the AS+CT scanner with a slice thickness of 3 mm were obtained from clinical practice. Experimental results demonstrate that our proposed method improves the accuracy of lesion detection for better treatment planning.

scholarly journals Dr. Agent: Clinical predictive model via mimicked second opinions

2020 ◽  
Vol 27 (7) ◽  
pp. 1084-1091
Author(s):  
Junyi Gao ◽  
Cao Xiao ◽  
Lucas M Glass ◽  
Jimeng Sun
Keyword(s):  
Health Status ◽  
Complex Disease ◽  
Medical Information ◽  
Prediction Models ◽  
Relevant Information ◽  
Disease Diagnosis ◽  
Mortality Prediction ◽  
Clinical Prediction ◽  
Phenotype Classification ◽  
Clinical Predictive Model

Abstract Objective Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training 2 agents with different focuses: the primary agent studies the most recent visit of the patient to learn the current health status, and then the second-opinion agent considers the entire patient history to obtain a more global view. Materials and Methods Our approach Dr. Agent augments recurrent neural networks with 2 policy gradient agents. Moreover, Dr. Agent is customized with various patient demographics information and learns a dynamic skip connection to focus on the relevant information over time. We trained Dr. Agent to perform 4 clinical prediction tasks on the publicly available MIMIC-III (Medical Information Mart for Intensive Care) database: (1) in-hospital mortality prediction, (2) acute care phenotype classification, (3) physiologic decompensation prediction, and (4) forecasting length of stay. We compared the performance of Dr. Agent against 4 baseline clinical predictive models. Results Dr. Agent outperforms baseline clinical prediction models across all 4 tasks in terms of all metrics. Compared with the best baseline model, Dr. Agent achieves up to 15% higher area under the precision-recall curve on different tasks. Conclusions Dr. Agent can comprehensively model the long-term dependencies of patients’ health status while considering patients’ demographics using 2 agents, and therefore achieves better prediction performance on different clinical prediction tasks.

A New Framework for the Accurate FE Model of Bone Tissue Based on CT Images

2010 ◽  
Vol 44-47 ◽  
pp. 1612-1616
Author(s):  
Xiao Hui Huang ◽  
Guo Qun Zhao ◽  
Wen Guang Liu ◽  
Pei Lai Liu
Keyword(s):  
Bone Tissue ◽  
Material Property ◽  
Geometric Model ◽  
Automatic Segmentation ◽  
Region Of Interest ◽  
Ct Images ◽  
Fe Model ◽  
Volume Of Interest ◽  
Smooth Operator ◽  
New Framework

The frameworks for finite element (FE) model of bone tissue available in pervious literatures, to some extent, are expert-oriented and give rise to a considerable deviation in geometric model and assignment of material property. The objective of this study is to develop a new framework to reconstruct accurate individual bone FE model based on CT images rapidly and conveniently. In image-processing, automatic segmentation of the region of interest (ROIs) improves the efficiency. The idea of enclosed volume of interest (VOI) overcomes the drawback of geometric ambiguity in Marching Cube (MC) method. Geometric model is easily obtained by a STL translator and smooth operator in home-made program. In the material property assignment, two templates for hexahedron and tetrahedron FE models, respectively, are put forth to smoothing an abrupt change of material property in the region from cortical to cancellous. K-mean algorithm is introduced to cluster material properties to improve partition performance. Finally, the new framework is demonstrated by the implementation of a femoral FE model.

scholarly journals Comparison of Lesion Detection and Quantification in MAP Reconstruction with Gaussian and Non-Gaussian Priors

2006 ◽  
Vol 2006 ◽  
pp. 1-10 ◽  
Author(s):  
Jinyi Qi
Keyword(s):  
Region Of Interest ◽  
Lesion Detection ◽  
Emission Tomography ◽  
Reconstruction Methods ◽  
Positron Emission ◽  
Hotelling Observer ◽  
Sharp Edges ◽  
Gaussian Prior ◽  
Non Gaussian ◽  
Detection And Quantification

Statistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior are Gaussian priors, whose logarithm has a quadratic form. Gaussian priors are relatively easy to analyze. It has been shown that the effect of a Gaussian prior can be approximated by linear filtering a maximum likelihood (ML) reconstruction. As a result, sharp edges in reconstructed images are not preserved. To preserve sharp transitions, non-Gaussian priors have been proposed. However, their effect on clinical tasks is less obvious. In this paper, we compare MAP reconstruction with Gaussian and non-Gaussian priors for lesion detection and region of interest quantification using computer simulation. We evaluate three representative priors: Gaussian prior, Huber prior, and Geman-McClure prior. We simulate imaging a prostate tumor using positron emission tomography (PET). The detectability of a known tumor in either a fixed background or a random background is measured using a channelized Hotelling observer. The bias-variance tradeoff curves are calculated for quantification of the total tumor activity. The results show that for the detection and quantification tasks, the Gaussian prior is as effective as non-Gaussian priors.

scholarly journals 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Shadi AlZu'bi ◽  
Abbes Amira
Keyword(s):  
Multiresolution Analysis ◽  
Markov Models ◽  
Automatic Segmentation ◽  
Region Of Interest ◽  
Computation Time ◽  
Comparison Study ◽  
Volume Segmentation ◽  
Statistical Approaches ◽  
3D Volume ◽  
2D And 3D

3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations.

scholarly journals Efficiency of LSB steganography on medical information

2021 ◽  
Vol 11 (5) ◽  
pp. 4157
Author(s):  
Oluwakemi Christiana Abikoye ◽  
Roseline Oluwaseun Ogundokun
Keyword(s):  
Information Security ◽  
Patient Information ◽  
Medical Information ◽  
Disease Diagnosis ◽  
Medical Field ◽  
Medical Practitioners ◽  
Digital World ◽  
Complete Knowledge ◽  
Lsb Technique ◽  
Audio Video

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder.

scholarly journals Detection of Cotton Leaf Disease Using Image Processing Techniques

2021 ◽  
Vol 2062 (1) ◽  
pp. 012009
Author(s):  
Sushreeta Tripathy
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Region Of Interest ◽  
Texture Features ◽  
Disease Diagnosis ◽  
Cotton Leaf ◽  
Diagnosis System ◽  
Image Processing Techniques ◽  
Plant Disease Diagnosis ◽  
Processing Techniques

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

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.

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