Analysis of Nanopore Structure Images Using MATLAB Software

The importance of nanopores increases with time due to their application. For instance, nanopores may be used to sense molecules like DNA and RNA, single proteins, etc. Sequencing by nanopore has also a possibility to be a direct, fast, and inexpensive DNA sequencing tool. Diameters of nanopores are the main keys for mentioned sensing processes. Three segmenting methods used in this study namely Thresholding, Gaussian Mixture Model-Expectation Maximization (GMM-EM) and Hidden Markov Random Field-Expectation Maximization (HMRF-EM). These methods applied on three SEM nanopore images after enhancing them through obtaining optimum parameters of CLAHE contrast-enhanced method to give high PSNR. The results of the Rand index and time of running code show that the HMRF-EM is better than GMM-EM. Hence, their segmented images are used to find out nanopore parameters including total counting pores, diameter, and porosity. The results of porosity were in good agreement with former investigations. Consequently, the HMRF-EM segmenting technique with procedures utilized in this study using image processing for finding porosity gives promising results among other examined methods

Segmentation and Estimation of Brain Tumor Volume in Magnetic Resonance Images Based on T2-Weighted using Hidden Markov Random Field Algorithm

A brain tumor is an abnormal growth of tissue in the brain. The segmentation of brain tumors, which has been manually achieved from magnetic resonance images (MRI) is a decisive and time-consuming task. Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the tumor size, position, and growth pattern. The accuracy and timeliness of detecting a brain tumor are vital factors to achieve the success in diagnosis and treatment of brain tumor. Therefore, segmentation and estimation of volume of brain tumor have been deemed a challenge mission in medical image processing. This paper aims to present a new approach, to improve the segmentation of brain tumors form T2-weighted MRI images using hidden Markov random fields (HMRF) and threshold method. We calculate the volume of the tumor using a new approach based on 2D images measurements and voxel space. The accuracy of segmentation is computed by using the ROC method. In order to validate the proposed approach a comparison is achieved with a manual method using Mango software. This comparison reveals that the noise or impurities in measurement of tumor volume are less in the proposed approach than in Mango softwar

Low price foot pressure distribution screening technique: Optical podoscope with accurate foot print segmentation using hidden Markov random field model

Copyright © 2020 The Author(s). Background: Foot pressure assessment systems are widely used to diagnose foot pathologies. The human foot plays an important role in maintaining the biomechanical function of the lower extremities which includes the provision of balance and stabilization of the body during gait. Objective: There are different types of assessment tools with different capabilities which are discussed in detail in this paper. In this project, we introduce a new camera-based pressure distribution estimation system which can give a numerical estimation in addition to giving a visual illustration of pressure distribution of the sole. Material and Methods: In this analytical study we proposed an accurate Foot Print segmentation using hidden Markov Random Field model. In the first step, an image is captured from the traditional Podoscope device. Then, the HMRF-EM image segmentation scheme applies to extract the contacting part of the sole to the ground. Finally, based on a simple calibration method, per mm2, pressure estimates to give an accurate pressure distribution measure. Results: A significant and usable estimation of foot pressure has been introduced in this article. The main drawback of introduced systems is the low resolution of sensors which is solved using a high resolution camera as a sensor. Another problem is the patchy edge extracted by the systems which is automatically solved in the proposed device using an accurate image segmentation algorithm. Conclusion: We introduced a camera-based plantar pressure assessment tool which uses HMRF-EM-based method has been explained in more detail which gives a brilliant sole segmentation from the captured images.“Tosee Tadbir Tavan Teb” company under Grant No.IR-0120151-101. The compa- ny’s registration number in Iran is 40697

A Probabilistic Adaptive Cerebral Cortex Segmentation Algorithm for Magnetic Resonance Human Head Scan Images

The total efficiency of Magnetic Resonance Imaging (MRI) results in the need for human involvement in order to appropriately detect information contained in the image. Currently, there has been a surge in interest in automated algorithms that can more precisely divide medical image structures into substructures than prior attempts. Instant segregation of cerebral cortex width from MRI scanned images is difficult due to noise, Intensity Non-Uniformity (INU), Partial Volume Effects (PVE), MRI's low resolution, and the very complicated architecture of the cortical folds. In this paper, a Probabilistic Adaptive Cerebral Cortex Segmentation (PACCS) approach is proposed for segmenting brain areas of T1 weighted MRI of human head images. Skull Stripping (SS), Brain Hemisphere Segmentation (BHS) and CCS are the three primary processes in the suggested technique. In step 1, Non-Brain Cells (NBC) is eliminated by a Contour-Based Two-Stage Brain Extraction Method (CTS-BEM). Step 2 details a basic BHS technique for Curve Fitting (CF) detection in MRI human head images. The left and right hemispheres are divided using the discovered Mid-Sagittal Plane (MSP). At last, to enhance a probabilistic CCS structure with adjustments such as prior facts change to remove segmentation bias; the creation of express direct extent training; and a segmentation version based on a regionally various Gaussian Mixture Model- Hidden Markov Random Field – Expectation Maximization (GMM-HMRF-EM). The underlying partial extent categorization and its interplay with found image intensities are represented as a spatially correlated HMRF within the GMM-HMRF-EM method. The proposed GMM-HMRF method estimates HMRF parameters using the EM technique. Finally, the outcomes of segmentation are evaluated in terms of precision, recall, specificity, Jaccard Similarity (JS), and Dice Similarity (DS). The proposed method works better and more consistently than the present locally Varying MRF (LV-MRF), according to the experimental findings obtained by using the suggested GMM-HMRF-EM methodology to 18 individuals' brain images

Machine Learning Strategies to Analyze Quantitative Ultrasound Multi-Parametric Images for Prediction of Therapy Response in Breast Cancer Patients

In this thesis project, two novel machine learning strategies were investigated to predict tumor response to neoadjuvant chemotherapy (NAC) at pre-treatment using quantitative ultrasound (QUS) multi-parametric images. The ultrasound data for analytical development and evaluation of the methodologies investigated in this project were acquired from 181 patients diagnosed with locally advanced breast cancer (LABC) and planned for NAC followed by surgery. The QUS multi-parametric images were generated using spectral analyses on the raw ultrasound radiofrequency (RF) data acquired before starting the NAC. In the first machine learning approach investigated in this project, distinct intra-tumor regions were identified within the parametric maps using a hidden Markov random field (HMRF) and its expectation-maximization (EM) algorithm. Several hand-crafted features characterizing the tumor, intra-tumor regions, and the tumor margin were extracted from different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker for response prediction. Evaluation results on an independent test set indicated that the developed biomarker using the characteristics of intra-tumor regions and tumor margin in conjunction with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patients at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In the second machine learning approach investigated in this project, two deep convolutional neural network (DCNN) architectures including the residual network (ResNet) and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. Results demonstrated that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had a superior performance with an accuracy of 0.88 and an AUC of 0.86 on the independent test set. Also, survival analysis demonstrated a statistically significant difference between survival curves of the two response cohorts identified at pre-treatment based on both the conventional machine learning method and the deep learning model. Obtained results in this study demonstrated a great promise of QUS multi-parametric imaging integrated with both unsupervised learning methods in identifying distinct breast cancer intra-tumor regions and traditional classification techniques, as well as deep convolutional neural networks in predicting tumor response to NAC prior to start of treatment

Machine Learning Strategies to Analyze Quantitative Ultrasound Multi-Parametric Images for Prediction of Therapy Response in Breast Cancer Patients

In this thesis project, two novel machine learning strategies were investigated to predict tumor response to neoadjuvant chemotherapy (NAC) at pre-treatment using quantitative ultrasound (QUS) multi-parametric images. The ultrasound data for analytical development and evaluation of the methodologies investigated in this project were acquired from 181 patients diagnosed with locally advanced breast cancer (LABC) and planned for NAC followed by surgery. The QUS multi-parametric images were generated using spectral analyses on the raw ultrasound radiofrequency (RF) data acquired before starting the NAC. In the first machine learning approach investigated in this project, distinct intra-tumor regions were identified within the parametric maps using a hidden Markov random field (HMRF) and its expectation-maximization (EM) algorithm. Several hand-crafted features characterizing the tumor, intra-tumor regions, and the tumor margin were extracted from different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker for response prediction. Evaluation results on an independent test set indicated that the developed biomarker using the characteristics of intra-tumor regions and tumor margin in conjunction with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patients at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In the second machine learning approach investigated in this project, two deep convolutional neural network (DCNN) architectures including the residual network (ResNet) and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. Results demonstrated that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had a superior performance with an accuracy of 0.88 and an AUC of 0.86 on the independent test set. Also, survival analysis demonstrated a statistically significant difference between survival curves of the two response cohorts identified at pre-treatment based on both the conventional machine learning method and the deep learning model. Obtained results in this study demonstrated a great promise of QUS multi-parametric imaging integrated with both unsupervised learning methods in identifying distinct breast cancer intra-tumor regions and traditional classification techniques, as well as deep convolutional neural networks in predicting tumor response to NAC prior to start of treatment