Chan-Vese Algorithm Research Articles

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification. This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques. This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets. SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets. These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.

Joint Reconstruction-Segmentation on Graphs

Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations. A recent approach for solving such tasks is to perform this reconstruction jointly with the segmentation, using each to guide the other. However, this work has so far employed relatively simple segmentation methods, such as the Chan–Vese algorithm. In this paper, we present a method for joint reconstruction-segmentation using graph-based segmentation methods, which have been seeing increasing recent interest. Complications arise due to the large size of the matrices involved, and we show how these complications can be managed. We then analyze the convergence properties of our scheme. Finally, we apply this scheme to distorted versions of “two cows” images familiar from previous graph-based segmentation literature, first to a highly noised version and second to a blurred version, achieving highly accurate segmentations in both cases. We compare these results to those obtained by sequential reconstruction-segmentation approaches, finding that our method competes with, or even outperforms, those approaches in terms of reconstruction and segmentation accuracy.

Deep-Learning-Based Real-Time and Automatic Target-to-Background Ratio Calculation in Fluorescence Endoscopy for Cancer Detection and Localization.

Esophageal adenocarcinoma (EAC) is a deadly cancer that is rising rapidly in incidence. The early detection of EAC with curative intervention greatly improves the prognoses of patients. A scanning fiber endoscope (SFE) using fluorescence-labeled peptides that bind rapidly to epidermal growth factor receptors showed a promising performance for early EAC detection. Target-to-background (T/B) ratios were calculated to quantify the fluorescence images for neoplasia lesion classification. This T/B calculation is generally based on lesion segmentation with the Chan–Vese algorithm, which may require hyperparameter adjustment when segmenting frames with different brightness and contrasts, which impedes automation to real-time video. Deep learning models are more robust to these changes, while accurate pixel-level segmentation ground truth is challenging to establish in the medical field. Since within our dataset the ground truth contained only a frame-level diagnosis, we proposed a computer-aided diagnosis (CAD) system to calculate the T/B ratio in real time. A two-step process using convolutional neural networks (CNNs) was developed to achieve automatic suspicious frame selection and lesion segmentation for T/B calculation. In the segmentation model training for Step 2, the lesion labels were generated with a manually tuned Chan–Vese algorithm using the labeled and predicted suspicious frames from Step 1. In Step 1, we designed and trained deep CNNs to select suspicious frames using a diverse and representative set of 3427 SFE images collected from 25 patient videos from two clinical trials. We tested the models on 1039 images from 10 different SFE patient videos and achieved a sensitivity of 96.4%, a specificity of 96.6%, a precision of 95.5%, and an area under the receiver operating characteristic curve of 0.989. In Step 2, 1006 frames containing suspicious lesions were used for training for fluorescence target segmentation. The segmentation models were tested on two clinical datasets with 100 SFE frames each and achieved mean intersection-over-union values of 0.89 and 0.88, respectively. The T/B ratio calculations based on our segmentation results were similar to the manually tuned Chan–Vese algorithm, which were 1.71 ± 0.22 and 1.72 ± 0.28, respectively, with a p-value of 0.872. With the graphic processing unit (GPU), the proposed two-step CAD system achieved 50 fps for frame selection and 15 fps for segmentation and T/B calculation, which showed that the frame rejection in Step 1 improved the diagnostic efficiency. This CAD system with T/B ratio as the real-time indicator is designed to guide biopsies and surgeries and to serve as a reliable second observer to localize and outline suspicious lesions highlighted by fluorescence probes topically applied in organs where cancer originates in the epithelia.

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis.

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.

New Hybrid Method for Left Ventricular Ejection Fraction Assessment from Radionuclide Ventriculography Images.

In this paper, we propose a new efficient method of radionuclide ventriculography image segmentation to estimate the left ventricular ejection fraction. This parameter is an important prognostic factor for diagnosing abnormal cardiac function. The proposed method combines the Chan-Vese and the mathematical morphology algorithms. It was applied to diastolic and systolic images obtained from the Nuclear Medicine Department of Salah AZAIEZ Institute. In order to validate our proposed method, we compare the obtained results to those of two methods present in the literature. The first one is based on mathematical morphology, while the second one uses the basic Chan-Vese algorithm. To evaluate the quality of segmentation, we compute accuracy, positive predictive value and area under the ROC curve. We also compare the left ventricle ejection fraction estimated by our method to that of the reference given by the software of the gamma-camera and validated by the expert, using Pearson's correlation coefficient, ANOVA test and linear regression. Static results show that the proposed method is very efficient for the detection of the left ventricle. The accuracy was 98.60%, higher than that of the other two methods (95.52% and 98.50%). Likewise, the positive predictive value was the highest (86.40% vs. 83.63% 71.82%). The area under the ROC curve was also the most important (0.998% vs. 0.926% 0.919%). On the other hand, Pearson's correlation coefficient was the highest (99% vs. 98% 37%). The correlation was significantly positive (p<0.001).

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.

Chan-Vese Segmentation Of SEM Ferrite-Pearlite Microstructure And Prediction Of Grain Boundary

The image processing of microstructure for design, measure and control of metal processing has been emerging as a new area of research for advancement towards the development of Industry 4.0 framework. However, exact steel phase segmentation is the key challenge for phase identification and quantification in microstructure employing proper image processing tool. In this article, we report effectiveness of a region based segmentation tool, Chan-Vese in phase segmentation task from a ferrite- pearlite steel microstructure captured in scanning electron microscopy image (SEM) image. The algorithm has been applied on microstructure images and the results are discussed in light of the effectiveness of Chan-Vese algorithms on microstructure image processing and phase segmentation application. Experiments on the ferrite perlite microstructure data set covering a wide range of resolution revealed that the Chan-Vese algorithm is efficient in segmentation of phase region and predicting the grain boundary.

Chan-Vese segmentation of SEM ferrite-pearlite microstructure and prediction of grain boundary

The image processing of microstructure for design, measure and control of metal processing has been emerging as a new area of research for advancement towards the development of Industry 4.0 framework. However, exact steel phase segmentation is the key challenge for phase identification and quantification in microstructure employing proper image processing tool. In this article, we report effectiveness of a region based segmentation tool, Chan-Vese in phase segmentation task from a ferrite- pearlite steel microstructure captured in scanning electron microscopy image (SEM) image. The algorithm has been applied on microstructure images and the results are discussed in light of the effectiveness of Chan-Vese algorithms on microstructure image processing and phase segmentation application. Experiments on the ferrite perlite microstructure data set covering a wide range of resolution revealed that the Chan-Vese algorithm is efficient in segmentation of phase region and predicting the grain boundary.

Feature extraction from dermoscopy images for melanoma diagnosis

Melanoma is the deadliest type of skin cancer. It has been rising exponentially for the last few decades. If it is diagnosed and treated at its early stage, the survival rate is very high. To prevent the invasive biopsy technique, automated diagnosis of melanoma from dermoscopy images has become a hot research area for the last few decades. This paper proposes three new distinct and effective features with some existing features related to shape, size and color properties of dermoscopy images based on ABCD rule for melanoma detection. ABCD stands for Asymmetry, Border, Color, and Diameter of the skin lesion. A two-stage segmentation approach including Otsu algorithm and Chan–Vese algorithm for lesion segmentation is implemented in this paper. Dull-Razor algorithm removes the black and dark hair from the input images and artificial neural network classifier classifies the malignant and benign images based on the extracted features. Implementation result of the proposed approach achieves 98.2% overall classification accuracy with 98% sensitivity and 98.2% specificity. These promising results indicate that the proposed system is able to assist the dermatologists in early detection of melanoma.

Image Edge Detection Based on Neutrosophic Set Approach Combined with Chan–Vese Algorithm

Since edge detection is a field of study used by various disciplines, it is of vital importance to calculate it accuretly. In addition, an edge detection algorithm may be involved in many image processing phases. A recent and contemporary approach, neutrosophy is based on neutrosophic logic, neutrosophic probability, neutrosophic set and neutrosophic statistics. This method yields better results compared to various other optimization methods. Neutrosophic Set (NS) is based on the origin, nature and scope of neutralities. In NS, problems are separated into true, false and indeterminacy subsets. It helps solve indeterminate situations effectively. It has recently been used in the field of image processing as indeterminate situations are also encountered in this field. Chan–Vese (CV) model is one of the successful region-based segmentation methods. The present study proposes a new NS-based edge detection method using CV algorithm. The proposed method combines the philosophical view of NS with successful segmentation characteristics of CV model. Obtained edge detection results are compared with different edge detection methods. The performances of each method are analyzed by using Figure of Merit (FOM) and Peak Signal-To-Noise Ratio (PSNR). The results suggest that the proposed method displays a better performance assessment compared to the used well-known methods.

Autosegmentation of the rectum on megavoltage image guidance scans

Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

Automatic image segmentation method for cotton leaves with disease under natural environment

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour (GAC) algorithm, Chan-Vese (C-V) algorithm and Local Binary Fitting (LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.

Detection of Diabetes by Macrovascular Tortuosity of Superior Bulbar Conjunctiva.

More than 8% of world population have diabetes which causes long term complications such as retinopathy, neuropathy, nephropathy and foot ulcers. Growing patient numbers has prompted large scale screening methods to detect early symptoms of diabetes (rather than elevated blood glucose levels which is a late symptom). Vascular tortuosity (twisted and curved nature of blood vessels) in retinal fundus images has proven to reflect the effect of diabetes on macrovasculature. However, large scale patient screening using retinal fundus images has limitations due to the requirement of a retinal camera. Therefore, we hypothesize that the vasculature of superior bulbar conjunctiva which could be captured using a regular camera could be used to measure tortuosity instead of retinal fundus images enabling mass screening.To test this hypothesis, a total of 168 scleral images were acquired from 50 healthy subjects and 34 diabetic patients using a digital camera. The sclera region was segmented using Chan-Vese algorithm and macrovasculature of superior bulbar conjunctiva was segmented using B-COSFIRE filters. Results revealed that the superior bulbar conjunctival macrovascular tortuosity of diabetic patients was significantly less than that of non-diabetic group (p-value =0.015). A similar result was yielded (p-value =0.049) from a group of participants who were less than 40 years old which excluded the age related variation of tortuosity.

A novel hybrid model for two-phase image segmentation: GSA based Chan–Vese algorithm

The active contours without edges model of Chan and Vese (Chan and Vese, 2001), which has been accepted for two-phase image segmentation is one of the most widely-used methods. It is a region-based segmentation model that utilizes the techniques of curve evolvement and the level set method.Chan–Vese model is a strong and flexible method that is able to segment many types of images compared to other active contours. Nevertheless, improper initial contours may reveal the problem of the Chan–Vese model getting stuck in a local minimum. This situation often provides poor results for the Chan–Vese model. Particularly, this problem occurs in the images that have large intensity differences between local and global structures. In this paper, we present a novel hybrid approach to the Chan–Vese algorithm to bring a solution to the problem of segmentation of these images. The proposed approach is based on the Gravitational Search Algorithm (GSA) developed in Rashedi et al. (2009). The idea is to arrange the fitting energy minimization problem according to a heuristic optimization technique and provide satisfactory segmentation outcomes regardless of the choice of the initial contour.The proposed model has been tested on both several images taken from Weizmann dataset and suitable medical images for the local minima problem. Experiments on the suitable test images prove that the proposed GSA based Chan–Vese model is more accomplished and more robust when compared to the conventional Chan–Vese algorithm. The test results also denote that the proposed algorithm requires much smaller number of iterations (%75 less) to converge than the conventional Chan–Vese algorithm.

3-D segmentation of lung nodules using hybrid level sets

Lung nodule segmentation in CT images and its subsequent volume analysis can help determine the malignancy status of a lung nodule. While several efficient segmentation schemes have been proposed, only a few studies evaluated the segmentation's performance for large nodules. In this research, we contribute a semi-automatic system which is capable of performing robust 3-D segmentations on both small and large nodules with good accuracy. The target CT volume is de-noised with an anisotropic diffusion filter and a region of interest is selected around the target nodule on a reference slice. The proposed model performs nodule segmentation by incorporating a mean intensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptive technique using image intensity histogram to estimate the desired mean intensity of the nodule. The proposed system is validated on both lung nodules and phantoms collected from publicly available diverse databases. Quantitative and visual comparative analysis of the proposed work with the Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented. The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, the mean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved results suggest that the proposed method can be used for volume estimations of small as well as large-sized nodules.

Dinámica Pulmonar de Estructuras Anatómicasde Interés en Imágenes 4DCT

This paper presents an application of the Chan-Vese algorithm for a semi-automatic segmentation of anatomical structures of interest (lungs and lung tumor) in thorax 4DCT images, as well as its threedimensional reconstruction. Segmentations and reconstructions were performed in 10 CT images, which conform an inspiration-expiration cycle. The maximum displacement of the lung tumor was calculated using the reconstructions of the beginning of inspiration, beginning of expiration, and the voxel size information. The proposed method was able to succesfully segment the studied structures regardless of their size and shape. The threedimensional reconstruction allow us to visualize the dynamics of the structures of interest throughout the respiratory cycle. In the near future, we are expecting to be able to have more evidence of the good performance of the proposed segmentation approach, and to have feedback from a clinical expert, giving the fact that the knowledge of anatomical structures characteristics, such as their size and spatial location, may help in the planning of radiotherapy treatments (RT), optimizing the radiation dose to cancer cells and minimizing it in healthy organs. Therefore, the information found in this work maybe of interest for the planning of RT treatments.

A Proposal: Diabetic Related Complications and Image Processing Methodologies

Objectives: Diabetes is a major public health problem and mainly affects the eyes, the kidney and the foot. This work focuses on diabetic foot to device new strategies for early diagnosis of diabetic foot in hospitals from the analysis of thermal images. Method/Statistical Analysis: In the presence of a triggering factor, it may lead to ulceration and subsequent amputation. In many cases, development of diabetic foot disorders can be avoided or substantially delayed with adequate treatments that are provided at an early stage. Most of the people who are with Diabetes have some form of nerve damage. Apart from this Hyperthermia it is also a challenging issue in diabetes correlated to foot these days. A new algorithm called Global Region Based Chan-vese Algorithm is proposed to analyze the underlying problems present in acquired images. Findings: A pre-processing technique as well as a post-processing technique of image processing methods will be used for the development of the frame work which will be useful for easy analysis and as well as an education tool for layman. Improvements/Applications: In this approach The segmentation boundary is represented implicitly with a level set function, but in previously employed methods the initial curve can be placed anywhere in the image.

ROS-based ground stereo vision detection: implementation and experiments.

This article concentrates on open-source implementation on flying object detection in cluttered scenes. It is of significance for ground stereo-aided autonomous landing of unmanned aerial vehicles. The ground stereo vision guidance system is presented with details on system architecture and workflow. The Chan–Vese detection algorithm is further considered and implemented in the robot operating systems (ROS) environment. A data-driven interactive scheme is developed to collect datasets for parameter tuning and performance evaluating. The flying vehicle outdoor experiments capture the stereo sequential images dataset and record the simultaneous data from pan-and-tilt unit, onboard sensors and differential GPS. Experimental results by using the collected dataset validate the effectiveness of the published ROS-based detection algorithm.

Asthma Pattern Identification via Continuous Diaphragm Motion Monitoring

Ultrasound imaging has been widely used in bio-medical imaging diagnosis for a long history because of its merits: no radiation, high penetration depth, and real-time imagingcapability. In this paper, we propose an ultrasound-based system that monitors respiratory status of asthma subjects via detecting of diaphragm movement. This system implements Chan-Vese algorithm to accurately segment diaphragm area from ultrasound image sequences and extracts 1D breathing waveform by computing mutual information (MI) between two consecutive ultrasound frames. In addition, four types of respiratory signals are identified: normal breath, fast breath, apnoea, and cough, which are related to four symptoms of asthma attack and defined as the breathing templates used for early asthma detection. In experiments, the proposed system is evaluated with a public dataset from “Ultrasound image gallery” which contains nine ultrasound videos and our dataset collected by “Interson Seemore” probe which contains five ultrasound videos in the diaphragm area. The results show that Chan-Vese segmentation method is superior to the other three algorithms: adaptive thresholding, EM/MPM, and Fuzzy C Means (FCM), and MI is a feasible method to extract accurate respiratory signal and clear information of the phase of respiratory cycle from 2D images.