Segmentation Of Disease Research Articles

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC.

During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.

Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks

Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD and Stargardt atrophy are their end-stage outcomes. Efficient methods for segmentation and quantification of these atrophic lesions are critical for clinical research. In this study, we developed a deep convolutional neural network (CNN) with a trainable self-attended mechanism for accurate GA and Stargardt atrophy segmentation. Compared with traditional post-hoc attention mechanisms which can only visualize CNN features, our self-attended mechanism is embedded in a fully convolutional network and directly involved in training the CNN to actively attend key features for enhanced algorithm performance. We applied the self-attended CNN on the segmentation of AMD and Stargardt atrophic lesions on fundus autofluorescence (FAF) images. Compared with a preexisting regular fully convolutional network (the U-Net), our self-attended CNN achieved 10.6% higher Dice coefficient and 17% higher IoU (intersection over union) for AMD GA segmentation, and a 22% higher Dice coefficient and a 32% higher IoU for Stargardt atrophy segmentation. With longitudinal image data having over a longer time, the developed self-attended mechanism can also be applied on the visual discovery of early AMD and Stargardt features.

Leaf Disease Detection and Spray Pesticide Robot using Image Processing

Abstract: India is a land of agriculture and mainly known for growing variety of crops. Around half of the population in India depend on agriculture. Diseases to the crops may affect the livelihood of the farmers. In order to overcome this major problem, a robot that detects the leaf disease using image processing and Machine learning is deployed. This robot also monitors the field condition such as quality of crops and sprays the required amount pesticides for achieving the good yield in agriculture. The robot is designed using an advanced processor known as yolov5 which is integrated with machine learning model. The machine learning model with Image processing is trained with feature extraction, Segmentation and classification of disease using image processing.

An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique

Tomato leaf infections are a common threat to long-term tomato production that affects many farmers worldwide. Early detection, treatment, and solution of tomato leaf specificity are critical for promoting healthy tomato plant growth and ensuring ample supply and health security for the world’s geometric growth (population). The detection of plant leaf disease using computer-assisted technologies is prevalent these days. In this work, use the 1610 tomato leaf images of different classes from PlantVillage standard repository for the localization of objects. An effective Deep Learning (DL) modified Mask Region Convolutional Neural Network (Mask R-CNN) is proposed for the autonomous segmentation and detection of tomato plant leaf disease in this research. Intending to conserve memory space and computational expense, the suggested model adds a light head “Region Convolutional Neural Network (R-CNN)”. By varying the proportions of anchor in the RPN network and also changing the feature extraction topology, which improves the detection accuracy and computing the metric performance. The proposed technique is compared to existing state-of-the-art models to check if it is viable and robust. The outcomes of the suggested model achieved the results in terms of Mean Average Precision (mAP), F1-score, and accuracy of 0.88, 0.912, and 0.98, respectively. Furthermore, as the model’s ability increases with some parameters, the detection time for lesion detection is reduced by two times than the existing models.

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images.

BackgroundThe detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels.MethodsIn this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes.ResultsThe experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve.DiscussionThe results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

Automatic semantic segmentation and detection of vertebras and intervertebral discs by neural networks

Objective: To realize a tool for automatic segmentation and detection of lumbar disc degenerative disease and fractures in MRI images using convolutional neural networks. Material and methods: We developed, on one hand, a Picture Archiving and Communication System (PACS) with a DICOM viewer (Digital Imaging and Communications in Medicine) to extract training data and implement 2 CNN networks dedicated to the segmentation task and on the other hand, the analysis of simple lumbar pathologies on the other hand.Two hundred forty-four MRI scans T2 weighted sagittal were selected at the university hospital Pasteur 2 of Nice - France.After accomplishing segmentation and classification task for all structures of interest, we trained two neural networks (U-Net++ and Yolov5x) to segment and detect discs and vertebras. Results: Neural networks allowed semantic segmentations with a good accuracy of the order of 0.96 and 0.93 DICE index for intervertebral discs and vertebral bodies respectively.They are less effective in detecting of common pathologies (degenerative disc disease, disc herniation, vertebral fractures) with an area under the precision-recall curve of 0.88 for fractures and 0.76 for degenerative disc disease. Conclusion: Our work has shown good efficiency for the segmentation of vertebral bodies and intervertebral discs, but progress remains to be made in the detection of degenerative discopathy and vertebral fractures.On this last point, we believe that the lower performance can be attributed to a lack of training images and that better performances will be achieved with the increase of the training data set on our home made PACS. We will also provide other regions of interest and conditions.

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

PurposeThe purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomography (CT). Materials and methodsTwo annotated datasets of COVID-19 pneumonia (323,960 slices) and interstitial lung disease (ILD) (4,284 slices) were used. Annotated CT images were used to train a U-Net architecture to segment disease. All CT slices were reconstructed using both a lung kernel (LK) and a mediastinal kernel (MK). Three different trainings, resulting in three different models were compared for each disease: training on LK only, MK only or LK+MK images. Dice similarity scores (DSC) were compared using the Wilcoxon signed-rank test. ResultsModels only trained on LK images performed better on LK images than on MK images (median DSC = 0.62 [interquartile range (IQR): 0.54, 0.69] vs. 0.60 [IQR: 0.50, 0.70], P < 0.001 for COVID-19 and median DSC = 0.62 [IQR: 0.56, 0.69] vs. 0.50 [IQR 0.43, 0.57], P < 0.001 for ILD). Similarly, models only trained on MK images performed better on MK images (median DSC = 0.62 [IQR: 0.53, 0.68] vs. 0.54 [IQR: 0.47, 0.63], P < 0.001 for COVID-19 and 0.69 [IQR: 0.61, 0.73] vs. 0.63 [IQR: 0.53, 0.70], P < 0.001 for ILD). Models trained on both kernels performed better or similarly than those trained on only one kernel. For COVID-19, median DSC was 0.67 (IQR: =0.59, 0.73) when applied on LK images and 0.67 (IQR: 0.60, 0.74) when applied on MK images (P < 0.001 for both). For ILD, median DSC was 0.69 (IQR: 0.63, 0.73) when applied on LK images (P = 0.006) and 0.68 (IQR: 0.62, 0.72) when applied on MK images (P > 0.99). ConclusionReconstruction kernels impact the performance of deep learning-based models for lung disease segmentation. Training on both LK and MK images improves the performance.

Segmentation atrioventricular septal defect by using convolutional neural networks based on U-NET architecture

Congenital heart disease often occurs, especially in infants and fetuses. Fetal image is one of the issues that can be related to the segmentation process. The fetal heart is an important indicator in the process of structural segmentation and functional assessment of congenital heart disease. This study is very challenging due to the fetal heart has a relatively unclear structural anatomical appearance, especially in the artifacts in ultrasound images. There are several types of congenital heart disease that often occurs namely in septal defects it consists of the atrial septal defect, ventricular septal defect, and atrioventricular septal defect. The process of identifying the standard of the heart, especially the fetus, can be identified with a 2D ultrasound video in the initial steps to diagnose congenital heart disease. The process of diagnosis of fetal heart standards can be seen from a variety of spaces, i.e., 4 chamber views. In this study, the standard semantic segmentation process of the fetal heart is abnormal and normal in terms of the perspective of 4 chamber views. The validation evaluation results obtained in this study amounted to 99.79% pixel accuracy, mean iou 96.10%, mean accuracy 97.82%, precision 96.41% recall 95.72% and F1 score 96.02%.

An MS-ROI based Detection and Segmentation of Erythemato-Squamous Disease

An MS-ROI based Detection and Segmentation of Erythemato-Squamous Disease

Novel coronavirus pneumonia detection and segmentation based on the deep-learning method.

BackgroundSegmentation of coronavirus disease 2019 (COVID-19) lesions is a difficult task due to high uncertainty in the shape, size and location of the lesions. CT scan image is an important means of diagnosing COVID-19, but it requires doctors to observe a large number of scan images repeatedly to determine the patient’s condition. Moreover, the low contrast of CT scan and the presence of tissues such as blood vessels in the background increase the difficulty of diagnosis. To solve this problem, we proposed an improved segmentation model called the residual attention U-shaped network (ResAU-Net).MethodsA novel method to detect and segment coronavirus pneumonia was established based on the deep-learning algorithm. Firstly, the CT scan image was input, and lung segmentation was then realized by U-net. Then, the region of interest was selected by the minimum circumscribed rectangle clipping method. Finally, the proposed ResAU-Net, which includes attention module (AMB), residual module (RBM) and sub-pixel convolution module (SPCBM), was used to segment the infected area and generate the segmentation results.ResultsWe evaluated our model using cross-validation on 100 chest CT scans test images. The experimental results showed that our method achieved start-of-the-art performance on the pneumonia dataset. The mIoU and Dice cofficients of Lesion segmentation were 73.40%±2.24% and 84.5%±2.46%, and realize fast real-time processing.ConclusionsOur model can effectively solve the problems of poor segmentation accuracy in the segmentation of COVID-19 lesions, and the segmentation result image can effectively assist medical staff in the diagnosis and quantitative analysis of infection degree, and improve the screening and diagnosis efficiency of pneumonia.

Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease

Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

Design of airport road surface inspection system based on machine vision and deep learning

The specific objective of this study was to solve the problem of high risk and low efficiency of manual inspection of airport road surface. This paper describes the design of an Airport Road Surface Intelligent Inspection System Based on machine vision and deep learning. The system completes the airport pavement inspection through autonomous navigation. MobileNet-SSD and Mask R-CNN algorithms are used for target detection and semantic segmentation of airport pavement apparent disease and foreign object bebris in this paper. At the same time, topological feature extraction and pixel size measurement were carried out. Finally, the information is uploaded to the storage server and the target information is displayed on the Android mobile terminal. The inspection system can complete the work of airport pavement inspection and provide a new way for airport pavement health monitoring.

Identification of disease using deep learning and evaluation of bacteriosis in peach leaf

Bacteriosis is one of the most common and devastating diseases for peach crops all over the world. Timely identification of bacteriosis disease is necessary for reducing the usage of pesticides and minimize loss of crops. In this proposed work, convolutional neural network (CNN) models using deep learning and an imaging method is developed for bacteriosis detection from the peach leaf images. In the imaging method, disease affected area is quantified and an adaptive operation is applied to a selected suitable channel of the color image. Gray level slicing is done on pre-processed leaf images for segmentation and automatic identification of bacterial spot disease in peach crops. The datasets are augmented to make the algorithm more robust to different illumination conditions. The proposed work compares the result of imaging method and CNN method. Model architectures generated with different deep learning algorithms, had the best performance reaching an accuracy of 98.75%% identifying the corresponding peach leaf [bacterial and healthy] in 0.185 s per image. The test dataset is consist of images from real cultivation field and also from the laboratory conditions. The significantly high identification rate makes the model diagnostic or early warning tool, and an approach that could be further integrated with the unmanned aerial vehicle to operate in real farming conditions

Segmentation of Leaf Spots Disease in Apple Plants Using Particle Swarm Optimization and K-means Algorithm

Leaf spot disease is one of the causes of a decrease in apple production. Early detection of this disease will increase the quality and amount of apple production. Monitoring the health of apple plants in larger area is traditionally a job that requires a lot of time and effort. The use of drones to detect the leaf spot disease is an alternative technology to monitor the health of apple plants in large areas. The image of an apple leaf taken by a drone needs to be processed by segmenting the apple leaves into the infected and the healthy leaves to detect apple leaf spot disease. K-means clustering offers an algorithm that is simple, fast and works unsupervised to segment images compared to level set algorithms. Random selection of centroid will cause K-means to be trapped at the local optimum point and result in unsatisfactory image segmentation. To solve this problem, Particle Swarm Optimization offers a good solution to optimization problems to avoid convergence problems at the local optimum. Therefore, in this paper, we study how to segment leaf spot disease on apple leaves using the Particle Swarm Optimization and K-means algorithm. The objective function of K-means algorithm optimized by Particle Swarm Optimization is used for segmenting the leaf spot disease. The first step in this proposed method is to convert the leaf image from RGB to CIE L * a * b format. The a component’s of L * a * b format are taken and clustered by using the Particle Swarm Optimization. The global best of the Particle Swarm Optimization becomes an initial centroid of K-means algorithm. The experimental results show that the Particle Swarm Optimization-K-means (PSOK) has a better performance than the K-means algorithm in segmenting leaf spot disease.

HSO Based K-Means for Automatic Segmentation of Disease and Deficiency in Tomato Plant

Image segmentation is an important part in image processing because it split the given whole image into parts, among the parts we can choose which one is most important. There are wide range of image processing algorithms available among them K-means algorithm is very simple to understand is one of the simplest and Produce accurate result, even though it is simple and accurate it has some limitations that is we need to guess the value for K and randomly select K initial centroids among the given data points. To overcome this initialization problem a new method for image segmentation based on Harmonic search optimization (HSO) and K-means for deficiency detection was proposed. The performance of K-means algorithm is mainly based on the k value and k initial centroids of the clusters. Random initialization is followed in normal K-means algorithm due to this normal k-means take lots of time to produce correct. A new method called HSO based K-means is proposed to speed up the initialization process. The proposed algorithms exploit an initial step derived from the HSO, considering Otsu method as the objective function. After finding the cluster centers using HSO, K-means algorithm is initialised with these cluster centers. Finally, segmentation result is compared with normal K-Means and EM segmentation algorithm our proposed HSO based K-means algorithm gives better result than others.

A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease.

Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). In this study, experimental results were provided through the segmentation of Parkinson's disease (PD) MR images. Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MR images very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.

Challenge Faced in K-Means Clustering for the Choice of Automatic K-Value for Segmentation of Black Sigatoka Disease in Banana Leaves

Clustering is defined as grouping similar items . The three types of machine learning techniques are supervised, unsupervised and semi-supervised. In unsupervised technique, there are no class labels given to the input data. Clustering is a type of unsupervised learning technique. Recently clustering is applied in many fields such as medicine, agriculture, biology, computers, finance and robotics. Black sigatoka is a bacterial disease occurring commonly in banana plants .The research currently focuses on segmenting the disease area from non-diseased area.The segmentation class training is done via Trainable Weka Segmentation and we also do segmentation using k-means algorithm. In this paper we propose a novel approach for extraction of the black sigatoka diseased area on banana leaves from images using pixel color values and grouping them into their respective clusters accordingly. This is a segmentation cum clustering algorithm. The novel approach has been proposed to overcome the shortfall of k-means clustering when segmenting using automatic value selection for k-means by using silhouette values.Using this novel approach its easy to cluster and segment at the same time. The segmented image from this algorithm can be used in disease classification tasks.

Segmentation and Classification of Plant Disease using SVM–PSO in Cloud

Area of agriculture plant disease detection attracts is very important one, main role is diseases detection. To develop the plant diseases detection, it required to identify arrival of the diseases in the leaf and instruction to the agriculturalists. In this proposed work, a leaf disease detection system (LDDS) based on Otsu segment (OS) is developed to identify and classify the diseases in the set of leaves. Clustering scheme is offered from segmented image of the diseased leaf. Otsu segmentation is measured the size of segmented leaf are uploaded to less storage place. In observing location, the amounts are retrieved as well as the features are extracted from the original segmented image. The enhancement as well as classification is used to SVM based on PSO classifier. The overall design of this paper is LDDS take scan be calculated in terms of system efficiency and it is compared with the existing methods. The result indicates the research technique offers a whole detection accuracy of 90.5% and classification accuracy of 90.4%.

Novel data augmentation strategies to boost supervised segmentation of plant disease

Annotation of images in supervised learning is notably costly and time-consuming. In order to reduce this cost, our objective was to generate images from a small dataset of annotated images, and then use those synthesized images to help the network’s training process. In this article, we tackled for illustration with agricultural material the difficult segmentation task of apple scab on images of apple plant canopy by using convolutional neural networks. We devised two novel methods of generating data for this use case: one based on a plant canopy simulation and the other on Generative Adversatial Networks (GANs). As a result, we found that simulated data could provide an important increase in segmentation performance, up to a 17% increase of F1 score (a measure taking into account precision and recall), compared to segmenting with weights initialized on ImageNet. In this way, we managed to obtain, with small datasets, higher segmentation scores than the ones obtained with bigger datasets if using no such augmentations. Moreover, we left our annotated dataset of scab available for the plant science imaging community. The proposed method is of large applicability for plant diseases observed at a canopy scale.

A Review of Automated Methods for the Detection of Sickle Cell Disease.

Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes elaborate analysis of proper disease diagnosis after accurate detection followed by a classification of irregularities, which plays a vital role in the sickle cell disease diagnosis, treatment planning, and treatment outcome evaluation. Proper segmentation of complex cell clusters makes sickle cell detection more accurate and robust. Cell morphology has a key role in the detection of the sickle cell because the shapes of the normal blood cell and sickle cell differ significantly. This review emphasizes state-of-the-art methods and recent advances in detection, segmentation, and classification of sickle cell disease. We discuss key challenges encountered during the segmentation of overlapping blood cells. Moreover, standard validation measures that have been employed to yield performance analysis of various methods are also discussed. The methodologies and experiments in this review will be useful to further research and work in this area.