A genetically optimized level set approach to segmentation of thyroid ultrasound images

This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.     

Accurate segmentation of complex document image using digital shearlet transform with neutrosophic set as uncertainty handling tool

In any image segmentation problem, there exist uncertainties. These uncertainties occur from gray level and spatial ambiguities in an image. As a result, accurate segmentation of text regions from non-text regions (graphics/images) in mixed and complex documents is a fairly difficult problem. In this paper, we propose a novel text region segmentation method based on digital shearlet transform (DST). The method is capable of handling the uncertainties arising in the segmentation process. To capture the anisotropic features of the text regions, the proposed method uses the DST coefficients as input features to a segmentation process block. This block is designed using the neutrosophic set (NS) for management of the uncertainty in the process. The proposed method is experimentally verified extensively and the performance is compared with that of some state-of-the-art techniques both quantitatively and qualitatively using benchmark dataset.     

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.     

Individual tooth segmentation from CT images using level set method with shape and intensity prior

3D visualization of teeth from CT images provides important assistance for dentists performing orthodontic surgery and treatment. However, dental CT images present several major challenges for the segmentation of tooth, which touches with adjacent teeth as well as surrounding periodontium and jaw bones. Moreover, tooth contour suffers from topological changes and splits into several branches. In this work, we focus on the segmentation of individual teeth with complete crown and root parts. To this end, we propose adaptive active contour tracking algorithms: single level set method tracking for root segmentation to handle the complex image conditions as well as the root branching problem, and coupled level set method tracking for crown segmentation in order to separate the touching teeth and create the virtual common boundaries between them. Furthermore, we improve the variational level set method in several aspects: gradient direction is introduced into the level set framework to prevent catching the surrounding object boundaries; in addition to the shape prior, intensity prior is introduced to provide adaptive shrinking or expanding forces in order to deal with the topological changes. The test results for both tooth segmentation and 3D reconstruction show that the proposed method can visualize individual teeth with high accuracy and efficiency.     

Automated detection and recognition of thyroid nodules in ultrasound images using Improve Cascade Mask R-CNN

Multimedia Tools and Applications - Accurate diagnosis of thyroid nodules using ultrasonography heavily relies on the superb skills and rich experience of senior radiologists, considering the low...     

Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images

Most thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in ultrasound images. Numerous textural feature extraction methods are used to characterize these patterns to reduce the misdiagnosis rate. Thyroid nodules can be classified using the corresponding textural features. In this paper, six support vector machines (SVMs) are adopted to select significant textural features and to classify the nodular lesions of a thyroid. Experiment results show that the proposed method can correctly and efficiently classify thyroid nodules. A comparison with existing methods shows that the feature-selection capability of the proposed method is similar to that of the sequential-floating-forward-selection (SFFS) method, while the execution time is about 3-37 times faster. In addition, the proposed criterion function achieves higher accuracy than those of the F-score, T-test, entropy, and Bhattacharyya distance methods.     

Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

自适应水平集方法乳腺超声肿块分割应用

针对超声成像固有的噪声大、伪影斑点多、对比度低等特点, 在利用CV和LBF模型优点的基础上, 融合了动态变化制导的全局信息和局部信息, 在能量泛函演化过程中, 全局信息项和局部信息项的权重系数实时变化调整。实验结果表明, 与两种已有模型相比, 该方法能够较好地处理灰度非匀质乳腺超声图像的肿块病灶分割问题, 分割准确性和病灶边缘细节处理更好, 分割速度较快, 临床适用性更强。     

Content driven dimensionality reduction at block level in the design of an efficient classifier for spatial multi spectral images

In case of spatial multi spectral images, such as remotely sensed earth cover, there could be many classes in one entire frame covering a large spatial stretch, because of which meaningful dimensionality reduction cannot perhaps be realizable without trading off with the quality of classification. However most often one would encounter in such images, presence of only a few classes in a small neighborhood, which would enable to devise a very effective dimensionality reduction around that small neighborhood identified as a block. Based on this theme a new method for dimensionality reduction is proposed in this paper.

The method proposed divides the image into uniform non-overlapping windows/blocks. The few features that are essential in discriminating classes in a window are identified. Clustering is performed independently on each of the blocks with the reduced set of features. These clusters in the blocks are later merged to obtain an overall classification of the entire image. The efficacy of the method is corroborated experimentally.     

双层水平集描述眼底图像视杯视盘分割

目的 青光眼是一种可导致视力严重减弱甚至失明的高发眼部疾病。在眼底图像中,视杯和视盘的检测是青光眼临床诊断的重要步骤之一。然而,眼底图像普遍是灰度不均匀的,眼底结构复杂,不同结构之间的灰度重叠较多,受到血管和病变的干扰较为严重。这些都给视盘与视杯的分割带来很大挑战。因此,为了更准确地提取眼底图像中的视杯和视盘区域,提出一种基于双层水平集描述的眼底图像视杯视盘分割方法。方法 通过水平集函数的不同层级分别表示视杯轮廓和视盘轮廓,依据视杯与视盘间的位置关系建立距离约束,应用图像的局部信息驱动活动轮廓演化,克服图像的灰度不均匀性。根据视杯与视盘的几何形状特征,引入视杯与视盘形状的先验信息约束活动轮廓的演化,从而实现视杯与视盘的准确分割。结果 本文使用印度Aravind眼科医院提供的具有视杯和视盘真实轮廓注释的CDRISHTI-GS1数据集对本文方法进行实验验证。该数据集主要用来验证视杯及视盘分割方法的鲁棒性和有效性。本文方法在数据集上对视杯和视盘区域进行分割,取得了67.52%的视杯平均重叠率,81.04%的视盘平均重叠率,0.719的视杯F1分数和0.845的视盘F1分数,结果优于基于COSFIRE（combination of shifted filter responses）滤波模型的视杯视盘分割方法、基于先验形状约束的多相Chan-Vese（C-V）模型和基于聚类融合的水平集方法。结论 实验结果表明,本文方法能够有效克服眼底图像灰度不均匀、血管及病变区域的干扰等影响,更为准确地提取视杯与视盘区域。     

Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains

Due to the complicated structure of breast and poor quality of ultrasound images, accurately and automatically locating regions of interest (ROIs) and segmenting tumors are challenging problems for breast ultrasound (BUS) computer-aided diagnosis systems. In this paper, we propose a fully automatic BUS image segmentation approach for performing accurate and robust ROI generation, and tumor segmentation. In the ROI generation step, the proposed adaptive reference point (RP) generation algorithm can produce the RPs automatically based on the breast anatomy; and the multipath search algorithm generates the seeds accurately and fast. In the tumor segmentation step, we propose a segmentation framework in which the cost function is defined in terms of tumor?s boundary and region information in both frequency and space domains. First, the frequency constraint is built based on the newly proposed edge detector which is invariant to contrast and brightness; and then the tumor pose, position and intensity distribution are modeled to constrain the segmentation in the spatial domain. The well-designed cost function is graph-representable and its global optimum can be found. The proposed fully automatic segmentation method is applied to a BUS database with 184 cases (93 benign and 91 malignant), and the performance is evaluated by the area and boundary error metrics. Compared with the newly published fully automatic method, the proposed method is more accurate and robust in segmenting BUS images.     

Detection and classification of lung nodules in chest X-ray images using deep convolutional neural networks

Lung nodule classification is one of the main topics related to computer-aided detection systems. Although convolutional neural networks (CNNs) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in chest X-ray (CXR) images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic curve of 7.76 considering 10 false positives per image (FPPI), and sensitivity values of 73.1% and 79.6% with 2 and 5 FPPI, respectively.     

Automatic data clustering using continuous action-set learning automata and its application in segmentation of images

Most of the proposed algorithms to solve the dynamic clustering problem are based on nature inspired meta-heuristic algorithms. In this paper a different reinforcement based optimization approach called continuous action-set learning automata (CALA) is used and a novel dynamic clustering approach called ACCALA is proposed. CALA is an optimization tool interacting with a random environment and learn the optimal action from the environment feedbacks. In this paper the dynamic clustering problem considered as a noisy optimization problem and the team of CALAs is used to solve this noisy optimization problem. To build such a team of CALAs this paper proposed a new representation of CALAs. Each automaton in this team uses its continuous action-set and defining a suitable action-set for each automaton has a great impact on the CALAs search behavior. In this paper we used the statistical property of data-sets and proposed a new method to automatically find an action-set for each automaton. The performance of ACCALA is evaluated and the results are compared with seven well-known automatic clustering techniques. Also ACCALA is used to perform automatic segmentation. The experimental results are promising and show that the proposed algorithm produced compact and well-separated clusters.     

Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed

Blob or granular object recognition is an image processing task with a rich application background, ranging from cell/nuclei segmentation in biology to nanoparticle recognition in physics. In this study, we establish a new and comprehensive framework for granular object recognition. Local density clustering and connected component analysis constitute the first stage. To separate overlapping objects, we further propose a modified watershed approach called the gradient-barrier watershed, which better incorporates intensity gradient information into the geometrical watershed framework. We also revise the marker-finding procedure to incorporate a clustering step on all the markers initially found, potentially grouping multiple markers within the same object. The gradient-barrier watershed is then conducted based on those markers, and the intensity gradient in the image directly guides the water flow during the flooding process. We also propose an important scheme for edge detection and fore/background separation called the intensity moment approach. Experimental results for a wide variety of objects in different disciplines – including cell/nuclei images, biological colony images, and nanoparticle images – demonstrate the effectiveness of the proposed framework.