Distance Regularized Level Set Research Articles

Automated Parasite’s Detection in Microscopic Images of Stools Using Distance Regularized Level Set Evolution Initialized with Hough Transform

Background and purpose: The analysis of biomedical microscopic images is carried out manually in medical laboratories. The manual analysis of clinical images lets to both repetitive tasks and management of huge amounts of data. This is tedious and times consuming for laboratory technicians. Inevitably, it is also prone to human errors. Our objective in this work is to contribute to the automation of the analysis of microscopic images of stools using Distance Regularized Level Set Evolution automatically initialized by Hough transform. Method: We firstly converted the microscopic images to edge maps using canny algorithm. Next, we located the parasite through circular Hough transform and draw circles around them. Those circles stand as initial contours of DRLSE. The contours evolve until they fit the boundaries of the parasites. The final extraction is performed using a complementary method based on the signed distance character of the level set function. Results: The Distance Regularized Level Set Evolution has been automatically initialized. We applied our method to the detection of intestinal parasites in microscopic images. Experimental results show accurate, efficient and less time consuming of our scheme compared to others recently proposed in the literature. Conclusion: This is a notable contribution to the automation of stools examination in the medical laboratories. In forthcoming works, we plan to include this segmentation process in an expert system of parasitic diseases diagnosis.

Local comparison of cup to disc ratio in right and left eyes based on fusion of color fundus images and OCT B-scans

Symmetry analysis of Optical Coherence Tomography (OCT) images in right and left eyes can lead to introduction of new biomarkers for early detection of eye diseases. In this study, we investigate the symmetry between two eyes by calculating local cup to disc ratio (CDR) from each B-scan based on fusion of fundus images and OCT B-scans. For this purpose, in the first phase, the OCT data of Optic Nerve Head (ONH) in right and left eyes are aligned by finding the equivalent B-scans of both eyes based on the fovea-ONH axes. Since the fovea-ONH axes in OCT data are not available, at first, left and right fundus images are aligned according to their automatically detected fovea-ONH axes. Then, OCT data are registered to corresponding fundus images based on maximum similarity between en-face OCT data and fundus image in each eye. This two-stage alignment procedure depends on 1) the blood vessels, automatically extracted by Hessian analysis of directional curvelet sub-bands, and 2) the disc contour of ONH in fundus images, detected by Distance Regularized Level Set Evolution (DRLSE) algorithm. In the second phase, in order to calculate the local CDRs, the disc and cup boundaries are extracted from the aligned B-scans in left and right eyes. The disc boundary is limited by Bruch-Membrane opening, and the cup boundary is defined by retinal layer border. Therefore, Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) layers are extracted using ridgelet transform to find the disc-edge point and cup-edge point in each B-scan. Finally, the ratio of summed areas of cups to summed areas of discs is calculated in corresponding local regions to find the local CDRs. Using this method, we can also introduce a new index called local volumetric CDR (VCDR) by dividing the volume of cup in a specific region to the corresponding volume of disc extracted from OCT images of ONH. Forty healthy OCT datasets of size 650 × 512 × 128 (acquired from Topcon 3D OCT-1000) and corresponding 1536 × 1612 fundus images were used in this study. In addition to point-by-point comparison of CDRs in equivalent B-scans of aligned OCTs, the CDRs in upper, middle and lower regions were calculated and the maximum symmetry is observed in middle region. In addition, using local VCDR, the symmetry of 3D OCTs of both eyes is analyzed in 24 volumetric sectors.

Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods

Accurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the CC from magnetic resonance (MR) images of the brain, namely: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method and the Distance Regularized Level Set Evolution (DRLSE) method. The pre-processing methods applied during research work were to improve the contrast, reduce noise and thus help segment the CC better. In this project, 3D CC models reconstructed based on the segmentations of cross-sections of MR images were also visualised. The results, as measured by quantitative tests of the similarity indice (SI) and overlap value (OV) are the best for the EM model (SI = 92%, OV = 82%) and are comparable to or better than those for other methods taken from a literature review. Furthermore, the properties of the EM model consisting in its ability to both expand and shrink at the same time allow segmentations to be better fitted in subsequent CC slices then in state-of-the art ACMs such as DRLSE or SBGFRLS. The CC contours from previous and subsequent iterations produced by the EM model can be used for initiation in subsequent or previous frames of MR images, which makes the segmentation process easier, particularly as the CC area can increase or decrease in subsequent MR image frames.

MRI Images Segmentation and 3D Reconstruction for Cerebral Cancer Detection

Abstract Cancer is a dense and an abnormal rapid multiplication (proliferation) of cells in the tissues of the human body. The brain tumor is one of the most dangerous and deadly tumors. Fortunately, the evolution of science has allowed us to create very efficient medical imaging techniques in order to discover this type of cancer. Chief among these techniques is magnetic resonance imaging (MRI) which is a very efficient technique compared to ultrasound. In this work, we are interested in the detection of this type of cancer allowing a three-dimensional (3D) reconstruction of MRI images. The segmentation methods used are based primarily on the Fuzzy C-Means algorithm that classifies and isolates parts of the brain tissue, and secondly on Distance Regularized Level Set Evolution technique for tumor detection. The obtained results show the effectiveness of this approach to detect brain tumor. The 3D reconstruction is finally carried out to better visualize the tumor as a whole and to detect its expansion. It is conducted using an indirect volume rendering method, which is the Marching Cubes algorithm.

Segmentation of lung fields from chest radiographs-a radiomic feature-based approach.

Precisely segmented lung fields restrict the region-of-interest from which radiological patterns are searched, and is thus an indispensable prerequisite step in any chest radiographic CADx system. Recently, a number of deep learning-based approaches have been proposed to implement this step. However, deep learning has its own limitations and cannot be used in resource-constrained settings. Medical systems generally have limited RAM, computational power, storage, and no GPUs. They are thus not always suited for running deep learning-based models. Shallow learning-based models with appropriately selected features give comparable performance but with modest resources. The present paper thus proposes a shallow learning-based method that makes use of 40 radiomic features to segment lung fields from chest radiographs. A distance regularized level set evolution (DRLSE) method along with other post-processing steps are used to refine its output. The proposed method is trained and tested using publicly available JSRT dataset. The testing results indicate that the performance of the proposed method is comparable to the state-of-the-art deep learning-based lung field segmentation (LFS) methods and better than other LFS methods.

Segmentation of the lumen and media‐adventitial borders in intravascular ultrasound images using a geometric deformable model

This study presents a geometric deformable model-based segmentation approach to segmentation of the intima and media-adventitial (MA) borders in sequential intravascular ultrasound (IVUS) images. The initial estimation of the vessel borders was done manually only for the first frame of each sequence. After the border initialisation, pre-processing including edge preservation, noise reduction, and dead zone preservation was successively performed on each IVUS frame. To improve segmentation performance, the image masks were determined preliminarily by local binary pattern-based mask initialisation. Then, the inner and outer borders were approximated using a modified distance regularised level set evolution model. The results showed superior performance of the suggested approach for estimating intima and MA layers from the IVUS images. The corresponding correlation coefficients of area, vessel perimeter, maximum vessel diameter, and maximum lumen diameter were r = 0.782, r = 0.716, r = 0.956, and r = 0.874 for the 20 MHz images, respectively, and r = 0.990, r = 0.995, r = 0.989, and r = 0.996 for the 45 MHz images, respectively. In addition, linear regression analysis indicated that the manual segmentation had significantly high similarity at r > 0.967 and r > 0.993 for 20 and 45 MHz images, respectively.

Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction.

To solve the problem of scoliosis recognition without a labeled dataset, an unsupervised method is proposed by combining the cascade gentle AdaBoost (CGAdaBoost) classifier and distance regularized level set evolution (DRLSE). The main idea of the proposed method is to establish the relationship between individual vertebrae and the whole spine with vertebral centroids. Scoliosis recognition can be transferred into automatic vertebral detection and segmentation processes, which can avoid the manual data-labeling processing. In the CGAdaBoost classifier, diversified vertebrae images and multifeature descriptors are considered to generate more discriminative features, thus improving the vertebral detection accuracy. After that, the detected bounding box represents an appropriate initial contour of DRLSE to make the vertebral segmentation more accurate. It is helpful for the elimination of initialization sensitivity and quick convergence of vertebra boundaries. Meanwhile, vertebral centroids are extracted to connect the whole spine, thereby describing the spinal curvature. Different parts of the spine are determined as abnormal or normal in accordance with medical prior knowledge. The experimental results demonstrate that the proposed method cannot only effectively identify scoliosis with unlabeled spine CT images but also have superiority against other state-of-the-art methods.

A CBCT series slice image segmentation method.

Images of industrial cone-beam computed tomography (CBCT) contain noise and beam hardening artifacts, which induce difficulty and low precision in segmenting regions of interest. The primary objective of this study is to improve the segmentation precision of CBCT series slice images. This paper presents a method based on the Phansalkar to segment CBCT series slice images precisely. First, the basics of the proposed method and the necessity of changing the local window size are analysed. The adaptive accumulated Phansalkar, which collects each pixel's classification results in different local windows, is proposed. Second, the bimodal distribution of the histogram is used to calculate the appropriate local window size for each pixel adaptively. Third, the characteristics of the accumulated probability (the accumulated classification results divided by the accumulated times) are analysed, from which an adaptive method is applied to segment the accumulated probability. Last, experiments are conducted on CBCT series slice images of three workpieces and one computer-aided design (CAD) model with internal defects. The proposed new method can segment CBCT images with noise and beam-hardening well. Moreover, for the segmentation of all four CBCT series slice images, the new method acquired the highest BF and AOM scores (1 and 0.9981) with the smallest standard deviation (0.0013) as compared with other existing methods including CMF (continuous max-flow/min cut), MS (mean-shift), DRLSE (distance regularized level set evolution), and ARKFCM (adaptively regularized kernel-based fuzzy c-means clustering). The experimental results support that our new method can more precisely segment CBCT series slice images with noise and artifacts than many existing methods. Thus, the new method has prospective application value and can provide valuable technical support for the industrial CBCT image post-processing system.

A Novel Curvature - Norm Based Centroid Initialized and Morphologically Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

Nuclear pleomorphism is considered to be one of the most significant shape based feature adapted in grading the cancer through the pathological studies of the H&E stained tissue slides. Microscopic study of manually extracting the feature is highly laborious and misleads the pathologists during grading. Digitization of the slides has given rise to various segmentation approaches to extract the nuclei shape to assess the degree of pleomorphism. Here, a novel approach of initializing and evolving the distance regularized level sets (DRLS) for the detection and segmentation of the nuclei has been presented. In this work, two major objectives have been achieved. First, a novel geometric approach has been devised for the detection of centroids of each nuclei in the occluded region and second, a shape prior model has been presented for the extraction of gradient information through morphological operations. The multiple level set implementation of the DRLS contours are initialized using the centroids detected and driven through the gradient computed. The proposed method has been experimented over the images of benign and malignant breast cancer tissue obtained from BeakHis dataset. A quantitative analysis of the results have shown that a 97% of object detection accuracy and 78% of overlap resolution has been achieved through the proposed model. A comparative study with that of geodesic active contours have indicated an improvement in the segmentation accuracy measure of 9-10 pixel difference.

An Innovative Practical Automatic Segmentation of Ultrasound Computer Tomography Images Acquired from USCT System

A 3D ultrasound computer tomography (USCT) device with a nearly isotropic and spatially invariant 3D point spread function has been constructed at Institute for Data Processing and Electronic (IPE), Karlsruhe Institute of Technology (KIT). This device is currently applied in clinical studies for breast cancer screening. In this paper, a new method to develop an automated segmentation algorithm for USCT acquired images is proposed. The method employs distance regularized level set evolutionary (DRLSE) active contours along with surface fitting extrapolation and 3D binary mask generation for fully automatic segmentation outcome. In the first stage of the proposed algorithm, DRLSE is applied to those 3D USCT slice images which contain breast and are less affected by noise and ring artifacts named as Cat2. The DRLSE segmentation results are employed to extrapolate the rest of slice images known as Cat1. To overcome defectively segmented slice images, a 3D binary mask is generated out of USCT attenuation images. The 3D binary mask is multiplied by the DRLSE-based segmentation results to form finally segmented 3D USCT images. The method was tested on 12 clinical dataset images. According to F-measure criterion, the proposed method shows higher performance than the previously proposed semiautomatic segmentation one.

Efficient initialisation of distance-regularised level set without re-initialisation scheme and quantitative evaluation of IMT in B mode ultrasound common carotid artery images

Segmentation of the Common Carotid Artery (CCA) wall is imperative for the estimation of the Intima Media Thickness (IMT) in B-mode longitudinal ultrasound images. IMT is considered as the prominent indicator of atherosclerosis and is defined as the distance between Lumen-Intima (LI) and Media-Adventitia (MA) interfaces of CCA vessel. This paper proposes an efficient initialisation scheme for Distance-Regularised Level Set without re-initialisation Evolution (DRLSE) method by utilising Fast Fuzzy C-Mean clustering (FFCM) technique to detect Li and MA interfaces accurately. Distance between the detected interfaces is determined by Polyline Distance Metric (PDM). Also, a quantitative and qualitative comparison among proposed method, manual method and snake method is presented. Experimental results show that the proposed method is robust in terms of region of interest selection (ROI), noise (speckle as well as salt and pepper) and images acquired from different machines. Inter-method, inter-operator and intra-operator variability have been studied by computing average, standard deviation, Coefficient of Variation (CV%) and via Wilcoxon’s signed-rank test (at p < 0.05). Reported results demonstrate close agreement between manual and proposed methods. The proposed method is semiautomatic and bears an advantage that a tight ROI is not required which encourages its use in clinical practice.

An efficient and robust hybrid method for segmentation of zebrafish objects from bright-field microscope images

Accurate segmentation of zebrafish from bright-field microscope images is crucial to many applications in the life sciences. Early zebrafish stages are used, and in these stages the zebrafish is partially transparent. This transparency leads to edge ambiguity as is typically seen in the larval stages. Therefore, segmentation of zebrafish objects from images is a challenging task in computational bio-imaging. Popular computational methods fail to segment the relevant edges, which subsequently results in inaccurate measurements and evaluations. Here we present a hybrid method to accomplish accurate and efficient segmentation of zebrafish specimens from bright-field microscope images. We employ the mean shift algorithm to augment the colour representation in the images. This improves the discrimination of the specimen to the background and provides a segmentation candidate retaining the overall shape of the zebrafish. A distance-regularised level set function is initialised from this segmentation candidate and fed to an improved level set method, such that we can obtain another segmentation candidate which preserves the explicit contour of the object. The two candidates are fused using heuristics, and the hybrid result is refined to represent the contour of the zebrafish specimen. We have applied the proposed method on two typical datasets. From experiments, we conclude that the proposed hybrid method improves both efficiency and accuracy of the segmentation of the zebrafish specimen. The results are going to be used for high-throughput applications with zebrafish.

Level set evolution driven by optimized area energy term for image segmentation

As a classic and famous active contour model for image segmentation, distance regularized level set evolution method avoids the process of re-initialization and can segment images flexibly, but it is easy to leak from objects with weak boundaries and fall into false boundaries. In this paper, an improved level set evolution model is proposed, in which an optimized area energy term combining a region growing matrix and an adaptive boundary indicator function is added to effectively detect boundaries for images with several adjacent targets and accelerate convergence at the same time. With an adaptive boundary indicator function involving a threshold defined by the standard deviation of images to be detected, this model can cross false boundaries and implement a correct segmentation for low contrast images. Meanwhile, the double-well potential function is optimized to make the model more stable. Experimental results on images of different objects have proved that the proposed model not only improves the precision of locating boundaries but also reduces the computational cost and works a stronger robustness than some other edge-based active contour models.

A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

A Combined Expectation Maximization and Marker Controlled Watershed Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model.

Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography for early detection of breast cancers. To facilitate the interpretation of these images, computer aided detection systems are being developed in which mass segmentation is an essential component for feature extraction and temporal comparisons. However, automated segmentation of masses is challenging because of the large variety in shape, size, and texture of these 3-D objects. In this paper, the authors aim to develop a computerized segmentation system, which uses a seed position as the only priori of the problem. A two-stage segmentation approach has been proposed incorporating shape information of training masses. At the first stage, a new adaptive region growing algorithm is used to give a rough estimation of the mass boundary. The similarity threshold of the proposed algorithm is determined using a Gaussian mixture model based on the volume and circularity of the training masses. In the second stage, a novel geometric edge-based deformable model is introduced using the result of the first stage as the initial contour. In a data set of 50 masses, including 38 malignant and 12 benign lesions, the proposed segmentation method achieved a mean Dice of 0.74 ± 0.19 which outperformed the adaptive region growing with a mean Dice of 0.65 ± 0.2 (p-value < 0.02). Moreover, the resulting mean Dice was significantly (p-value < 0.001) better than that of the distance regularized level set evolution method (0.52 ± 0.27). The supervised method presented in this paper achieved accurate mass segmentation results in terms of Dice measure. The suggested segmentation method can be utilized in two aspects: 1) to automatically measure the change in volume of breast lesions over time and 2) to extract features for a computer aided detection or diagnosis system.

A Method for Body Fat Composition Analysis in Abdominal Magnetic Resonance Images Via Self-Organizing Map Neural Network

Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in axial magnetic resonance (MR) images of the abdomen. Materials and Methods: A self-organizing map (SOM) neural network was designed to segment the adipose tissue from other tissues in the MR images. The segmentation of SAT and VAT was accomplished using a new level set method called distance regularized level set evolution (DRLSE). To evaluate the suggested method, the whole-body abdominal MRI was performed on 23 subjects, and three slices were selected for each case. Results: The results of the automatic segmentation were compared with those of the manual segmentation and previous artificial intelligent methods. According to the results, there was a significant correlation between the automatic and manual segmentation results of VAT and SAT. Conclusion: As the findings indicated, the suggested method improved detection of body fat. In this study, a fully automated abdominal adipose tissue segmentation algorithm was suggested, which used the SOM neural network and DRLSE level set algorithm. The proposed methodology was concluded to be accurate and robust with a significant advantage over the manual and previous segmentation methods in terms of speed and accuracy.

COASTLINE DETECTION USING FUSION OF OVER SEGMENTATION AND DISTANCE REGULARIZATION LEVEL SET EVOLUTION

Abstract. Coastline detection is a very challenging task in optical remote sensing. However the majority of commonly used methods have been developed for low to medium resolution without specification of the key indicator that is used. In this paper, we propose a new approach for very high resolution images using a specific indicator. First, a pre-processing step is carried out to convert images into the optimal colour space (HSV). Then, wavelet decomposition is used to extract different colour and texture features. These colour and texture features are then used for Fusion of Over Segmentation (FOOS) based clustering to have the distinctive natural classes of the littoral. Among these classes are waves, dry sand, wet sand, sea and land. We choose the mean level of high tide water, the interface between dry sand and wet sand, as a coastline indicator. To find this limit, we use a Distance Regularization Level Set Evolution (DRLSE), which automatically evolves towards the desired sea-land border. The result obtained is then compared with a ground truth. Experimental results prove that the proposed method is an efficient coastline detection process in terms of quantitative and visual performances.

Spatially constrained distance regularized level set evolution method for segmentation of hydrops fetalis from ultrasound fetal heart images

Hydrops Fetalis is an abnormal condition in the fetus by an accretion of unusual fluid or edema in two or more fetal cavities for instance ascites, pericardial and pleural effusion. Accumulation of atypical fluid above 2 mm needs extra attention of radiologists. Ultrasound imaging technique helps to diagnose the fluid accumulation within the pericardial tissues. The trained medicinal practitioners also find difficult to analyse the hydrops due to the low assessment gray scale ultrasound images. The segmentation of hydrops fetalis from the fetal heart ultrasound image helps the sonographists and pediatrists to diagnose easily and effectively at the early stage itself. The low quality and poor resolution ultrasound image, make the segmentation process difficult. We use the spatially constrained-distance regularized level set evolution algorithm for segmenting the hydrops region.

A neuro-fuzzy system for automated detection and classification of human intestinal parasites

Abstract Background and objective Human intestinal parasites are a major public health concern in tropical countries. The most reliable diagnosis of these parasites relies on the visual analysis of stool specimens. However, this method is time consuming, tedious, and prone to diagnosis error. Hence, the aim of this work is the automatic analysis of microscopic images for the classification of intestinal parasites. A combination of a fuzzy system and an artificial neural network leads to an artificial intelligence system appropriate for this task. Methods The approach is based on segmentation and training of a classifier. The input to the system is a microscopic image of a given stool sample with parasites. The parasite is firstly localized by the circular Hough transform and secondly, the distance regularized level set evolution is automatically initialized for segmentation. From the extracted parasite, we determined the histogram oriented region with feature vectors being of high interest. The dimension of feature vectors is reduced using linear discriminant analysis and is considered as input to the classifier. Finally, the neuro-fuzzy classifier is trained according to a speeded up scaled conjugate gradient algorithm. Results The proposed scheme has been applied for recognition and classification of twenty human intestinal parasites. The results demonstrate satisfactory classification for each of the twenty classes of parasites, with a recognition rate of 100%. The measure of root mean square error decreases with the number of epochs; subsequently, that error vanishes while training and evaluating the system, and confirms the high recognition rate achieved by the proposed classifier. Conclusions In this paper, we proposed a neuro-fuzzy system that classifies human intestinal parasites (Protozoa and Helminths) with high accuracy, independent to their stage of development (egg, cyst, or trophozoite). A satisfactory classification for twenty classes of parasites was successfully achieved, with possible extensions in future work.

Glioma Segmentation Using a Novel Unified Algorithm in Multimodal MRI Images

To achieve the better segmentation performance, we propose a unified algorithm for automatic glioma segmentation. In this paper, we first use spatial fuzzy c-mean clustering to estimate region-of-interest in multimodal MRI images, and then extract some seed points from there for region growing based on a new notion “affinity”. In the end, we design a two-step strategy to refine the glioma border with region merging and improved distance regularization level set method. In BRATS 2015 database, we evaluate the accuracy and robustness of our method with performance scores, including dice, positive predictive value (PPV), and sensitivity metrics, as well as Hausdorff and Euclidean distance (HD&ED). The high metric values (dice = 0.86, PPV = 0.90, and sensitivity = 0.84) and small distance errors (HD = 14.39 mm and ED = 3.31 mm) indicate a remarkable accuracy. Also, we observe the ranking is No.1 in terms of dice and PPV, comparing with the state-of-the-art methods. In addition, the robustness is also at a high-level due to the refinement structure. And Spearman’s rank coefficient test verities a significant correlation between the high-grade gliomas and low-grade gliomas. Overall, the proposed method is effective in segmenting gliomas in multimodal images or flair images, and has the potential in routine examinations of gliomas in daily clinical practice.