Candidate Segments Research Articles

First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning.

Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands.

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intra- and inter-patient shape variability, and uncertain existence of bowel gases and fiducial markers. In this paper, we propose a novel automatic segmentation framework using fully convolutional networks with boundary sensitive representation to address this challenging problem. Our novel segmentation framework contains three modules. First, an organ localization model is designed to focus on the candidate segmentation region of each organ for better performance. Then, a boundary sensitive representation model based on multi-task learning is proposed to represent the semantic boundary information in a more robust and accurate manner. Finally, a multi-label cross-entropy loss function combining boundary sensitive representation is introduced to train a fully convolutional network for the organ segmentation. The proposed method is evaluated on a large and diverse planning CT dataset with 313 images from 313 prostate cancer patients. Experimental results show that the performance of our proposed method outperforms the baseline fully convolutional networks, as well as other state-of-the-art methods in CT male pelvic organ segmentation.

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks

Background and ObjectiveThe spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Delimiting these OARs take a lot of time from medical specialists, plus the fact that involves a large team of professionals. Moreover, this task made slice-by-slice becomes an exhaustive and consequently subject to errors, especially in organs such as the spinal cord, which extend through several slices of the CT and requires precise segmentation. Thus, we propose, in this work, a computational methodology capable of detecting spinal cord in planning CT images. MethodsThe techniques highlighted in this methodology are adaptive template matching for initial segmentation, intrinsic manifold simple linear iterative clustering (IMSLIC) for candidate segmentation and convolutional neural networks (CNN) for candidate classification, that consists of four steps: (1) images acquisition, (2) initial segmentation, (3) candidates segmentation and (4) candidates classification. ResultsThe methodology was applied on 36 planning CT images provided by The Cancer Imaging Archive, and achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images, without any false positives reduction technique, in detection of spinal cord. ConclusionsIt is demonstrated the feasibility of the analysis of planning CT images using IMSLIC and convolutional neural network techniques to achieve success in detection of spinal cord regions.

Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model

Initial candidate segmentation is an important task in lung nodule detection. If an accurate segmentation is used, the false positives (FPs) can be decreased in subsequent stages and the desired region of each candidate can be distinguished. In this paper, a new hybrid method for nodule candidate segmentation and FPs reduction is proposed. First, the images are transferred to the neutrosophic domain. Then, three filters, named blob-like structure enhancement (BSE), line structure enhancement (LSE), and central adaptive medialness (CAM) filters, are used for filtering the output of the last step. Afterward, the outputs of BSE, LSE, and CAM filters are used for initial candidate detection and candidate segmentation, respectively. Also, line tracking method is proposed for extending the candidate voxels, and then, it is used for candidate segmentation. After feature extraction, the sparse coding is used for learning feature vector. In the last step, the generalized linear regression model (GLRM) is used for classification. The output of classifier for sensitivity and FP/scan is 98.32% and 2.8, respectively. AUC values of different link functions in GLRM, before and after feature learning, were also calculated, and the best value of AUC was obtained with probit link function after using the sparse coding method. The experimental results demonstrate the power of the proposed algorithm in nodules detection and false positive reduction.

Grasping Point Detection of Randomly Placed Fruit Cluster Using Adaptive Morphology Segmentation and Principal Component Classification of Multiple Features

Precise and rapid grasping point detection based on machine vision is one of the challenging problems in automatic sorting of randomly placed fruit clusters by robot. Grasping stalk of fruit cluster can improve grasping success probability and reduce fruit damage. For the problem that the segmentation of stalk candidates for randomly placed fruit cluster based on existing morphology algorithm tends to be low precision, an improved image segmentation algorithm based on adaptive morphology is proposed. According to edge distances defined based on minimum distance between edge point and unconnected components in minimum domain, the adaptive convolution kernel is constructed. In addition, a run analysis method with different and unordered labels is designed to reduce calculation time of edge distances. For the problem that it is difficult to describe and classify unconstraint stalk by existing features, an improved region classification algorithm based on principal components of multiple features is proposed. The descriptors based on features of object region are designed and principal components of multiple features are extracted based on variance contribution to improve precision and speed of stalk extraction. The proposed grasping point detection method of randomly placed fruit cluster based on improved morphology image segmentation and region classification algorithms is verified by experiments with grape clusters based on parallel robot sorting system. The results show that, compared with existing methods, the average precisions of segmentation and extraction for stalk increase by 9.89% and 2.17% respectively, the average precision and time of grasping point detection reach 94.50% and 2.01s respectively.

Power Line Extraction From Aerial Images Using Object-Based Markov Random Field With Anisotropic Weighted Penalty

The extraction of power line plays a key role in power line inspection by Unmanned Aerial Vehicles (UAVs). While it is challenging to extract power lines in aerial images because of the weak targets and the complex background. In this paper, a novel power line extraction method is proposed. First of all, we create a line segment candidate pool which contains power line segments and large amount of other line segments. Secondly, we construct the irregular graph model with these line segments as nodes. Then a novel object-based Markov random field with anisotropic weighted penalty (OMRF-AWP) method is proposed. It defines a new neighborhood system based on the irregular graph model and builds a new potential function by considering the region angle information. With the OMRF-AWP method, we can distinguish between the power line segments and other line segments. Finally, an envelope-based piecewise fitting (EPF) method is proposed to fit the power lines. Experimental results show that the proposed method has good performance in multiple scenes with complex background.

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data

This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre-processing steps that are dependent on a patient data and a scan protocol - it enables work with different types of CT scans; (ii)medial axis transform (MAT) post-processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta-analysis; and (iii) usability of the proposed method on whole-spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only). Our proposed method has been tested on our own dataset annotated by two mutually independent radiologists and has been compared to other published methods. This work is part of the ongoing complex project dealing with spine analysis and spine lesion longitudinal studies.

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Inpainting of Long Audio Segments With Similarity Graphs

We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap , i.e., the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.

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.

Mispronunciation Detection in Children's Reading of Sentences

This paper proposes an approach to automatically parse children's reading of sentences by detecting word pronunciations and extra content, and to classify words as correctly or incorrectly pronounced. This approach can be directly helpful for automatic assessment of reading level or for automatic reading tutors, where a correct reading must be identified. We propose a first segmentation stage to locate candidate word pronunciations based on allowing repetitions and false starts of a word's syllables. A decoding grammar based solely on syllables allows silence to appear during a word pronunciation. At a second stage, word candidates are classified as mispronounced or not. The feature that best classifies mispronunciations is found to be the log-likelihood ratio between a free phone loop and a word spotting model in the very close vicinity of the candidate segmentation. Additional features are combined in multifeature models to further improve classification, including: normalizations of the log-likelihood ratio, derivations from phone likelihoods, and Levenshtein distances between the correct pronunciation and recognized phonemes through two phoneme recognition approaches. Results show that most extra events were detected (close to 2% word error rate achieved) and that using automatic segmentation for mispronunciation classification approaches the performance of manual segmentation. Although the log-likelihood ratio from a spotting approach is already a good metric to classify word pronunciations, the combination of additional features provides a relative reduction of the miss rate of 18% (from 34.03% to 27.79% using manual segmentation and from 35.58% to 29.35% using automatic segmentation, at constant 5% false alarm rate).

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network

Background and Objective: White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. Methods: The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. Results: The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. Conclusions: It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions.

ISeg: an efficient algorithm for segmentation of genomic and epigenomic data

BackgroundIdentification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.ResultsWe designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences.ConclusionsWe have developed an efficient general-purpose segmentation tool and showed that it had comparable or more accurate results than many of the most popular segment-calling algorithms used in contemporary genomic data analysis. iSeg is capable of analyzing datasets that have both positive and negative values. Tunable parameters allow users to readily adjust the statistical stringency to best match the biological nature of individual datasets, including widely or sparsely mapped genomic datasets or those with non-normal distributions.

A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography.

Anatomical evaluation of multiple abdominal and thoracic organs is generally performed with computed tomography images. Owing to the large field-of-view of these images, automatic segmentation strategies are typically required, facilitating the clinical evaluation. Multi-atlas segmentation (MAS) strategies have been widely used with this process, requiring multiple alignments between the target image and the set of known datasets, and subsequently fusing the alignment results to obtain the final segmentation. Nonetheless, current MAS strategies apply a global alignment of a deformable object, per organ, subdividing the segmentation process into multiple ones and losing the spatial information among nearby organs. This paper presents a novel MAS approach. First, a coarse-to-fine method with multiple global alignments (one per organ) is used. To make the method spatially coherent, these individual organs' global transformations are then fused in one using a dense deformation field reconstruction strategy. Second, from the candidate segmentations obtained, the final segmentation is estimated through an organ-based label fusion approach. The proposed method is evaluated and compared against a conventional MAS strategy through the segmentation of twelve abdominal and thoracic organs from the VISCERAL Anatomy benchmark. Average Dice coefficients for the liver, spleen, lungs and kidneys are all higher than 90%, are around 85% for the aorta, trachea and sternum and 70% for the pancreas, urinary bladder and gallbladder. The novel MAS strategy, with dense deformation field reconstruction, shows competitive results against other state-of-the-art methods, proving its added value for the segmentation of abdominal and thoracic organs, mainly for highly variable organs.

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

In object-based image analysis (OBIA), the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimization (USPO). A popular USPO method does this through the optimization of a “global score” (GS), which minimizes intrasegment heterogeneity and maximizes intersegment heterogeneity. However, the calculated GS values are sensitive to the minimum and maximum ranges of the candidate segmentations. Previous research proposed the use of fixed minimum/maximum threshold values for the intrasegment/intersegment heterogeneity measures to deal with the sensitivity of user-defined ranges, but the performance of this approach has not been investigated in detail. In the context of a remote sensing very-high-resolution urban application, we show the limitations of the fixed threshold approach, both in a theoretical and applied manner, and instead propose a novel solution to identify the range of candidate segmentations using local regression trend analysis. We found that the proposed approach showed significant improvements over the use of fixed minimum/maximum values, is less subjective than user-defined threshold values and, thus, can be of merit for a fully automated procedure and big data applications.

Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection

In this paper, we propose a novel text-based traffic sign detection framework with two deep learning components. More precisely, we apply a fully convolutional network to segment candidate traffic sign areas providing candidate regions of interest (RoI), followed by a fast neural network to detect texts on the extracted RoI. The proposed method makes full use of the characteristics of traffic signs to improve the efficiency and accuracy of text detection. On one hand, the proposed two-stage detection method reduces the search area of text detection and removes texts outside traffic signs. On the other hand, it solves the problem of multi-scales for the text detection part to a large extent. Extensive experimental results show that the proposed method achieves the state-of-the-art results on the publicly available traffic sign data set: Traffic Guide Panel data set. In addition, we collect a data set of text-based traffic signs including Chinese and English traffic signs. Our method also performs well on this data set, which demonstrates that the proposed method is general in detecting traffic signs of different languages.

Proposal-Free Network for Instance-Level Object Segmentation.

Instance-level object segmentation is an important yet under-explored task. Most of state-of-the-art methods rely on region proposal methods to extract candidate segments and then utilize object classification to produce final results. Nonetheless, generating reliable region proposals itself is a quite challenging and unsolved task. In this work, we propose a Proposal-Free Network (PFN) to address the instance-level object segmentation problem, which outputs the numbers of instances of different categories and the pixel-level information on i) the coordinates of the instance bounding box each pixel belongs to, and ii) the confidences of different categories for each pixel, based on pixel-to-pixel deep convolutional neural network. All the outputs together, by using any off-the-shelf clustering method for simple post-processing, can naturally generate the ultimate instance-level object segmentation results. The whole PFN can be easily trained without the requirement of a proposal generation stage. Extensive evaluations on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate the effectiveness of the proposed PFN solution without relying on any proposal generation methods.

RETRACTED ARTICLE: Classification of sentence level sentiment analysis using cloud machine learning techniques

Cloud machine learning (CML) techniques offer contemporary machine learning services, with pre-trained models and a service to generate own personalized models. This paper presents a completely unique emotional modeling methodology for incorporating human feeling into intelligent systems. The projected approach includes a technique to elicit emotion factors from users, a replacement illustration of emotions and a framework for predicting and pursuit user’s emotional mechanical phenomenon over time. The neural network based CML service has better training concert and enlarged exactness compare to other large scale deep learning systems. Opinions are important to almost all human activities and cloud based sentiment analysis is concerned with the automatic extraction of sentiment related information from text. With the rising popularity and availability of opinion rich resources such as personal blogs and online appraisal sites, new opportunities and issues arise as people now, actively use information technologies to explore and capture others opinions. In the existing system, a segmentation ranking model is designed to score the usefulness of a segmentation candidate for sentiment classification. A classification model is used for predicting the sentiment polarity of segmentation. The joint framework is trained directly using the sentences annotated with only sentiment polarity, without the use of any syntactic or sentiment annotations in segmentation level. However the existing system still has issue with classification accuracy results. To improve the classification performance, in the proposed system, cloud integrate the support vector machine, naive bayes and neural network algorithms along with joint segmentation approaches has been proposed to classify the very positive, positive, neutral, negative and very negative features more effectively using important feature selection. Also to handle the outliers we apply modified k-means clustering method on the given dataset. It is used to cloud cluster the outliers and hence the label as well as unlabeled features is handled efficiently. From the experimental result, we conclude that the proposed system yields better performance than the existing system.

Quantitative trait loci responsible for the difference in γ-oryzanol content in brown rice between japonica-type and indica-type rice cultivars

γ-Oryzanol is a main oleophilic component in rice bran oil and has been well recognized as a good dietary supplement for human health, as well as having uses in industrial materials. japonica-type rice cultivars generally showed significantly higher contents of total γ-oryzanol in brown rice compared with indica-type cultivars, although within-group variation was significant. The objective of this study was to explore quantitative trait loci (QTLs) responsible for the difference in the γ-oryzanol content between japonica-type and indica-type rice cultivars, using recombinant inbred lines (RILs), backcross inbred lines (BILs), and corresponding chromosome segment substitution lines (CSSLs) derived from crosses between japonica-type and indica-type. Results from RILs and BILs showed that eight QTLs were detected with R2 from .09 to .16. Nine candidate regions for QTL were also suggested from corresponding CSSLs. These QTLs from RILs and BILs and the candidate regions from CSSLs were not overlapped, although one QTLs was mapped near the boundaries of the respective candidate region. At four QTLs and three candidate regions, alleles or segments from japonica-type caused higher contents than those from indica-type. On the other hand, at the other four QTLs and six candidate regions, alleles or segments from indica-type caused higher contents than those from japonica-type, which is a reverse result to the parental differences. This result strongly suggested that alleles with increasing effects on γ-oryzanol content could be accumulated not only from japonica-type but also from indica-type, leading to a potential for increase in γ-oryzanol content in future breeding programs.

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field.

Accurate follow-up of metastatic brain tumors has important implications for patient prognosis and management. The aim of this study was to develop and evaluate the accuracy of a semiautomated algorithm in detecting growing or shrinking metastatic brain tumors on longitudinal brain MRIs. We used 50 pairs of successive MR imaging datasets, 30 on 1.5T and 20 on 3T, containing contrast-enhanced 3D T1-weighted sequences. These yielded 150 growing or shrinking metastatic brain tumors. To detect them, we completed 2 major steps: 1) spatial normalization and calculation of the Jacobian operator field to quantify changes between scans, and 2) metastatic brain tumor candidate segmentation and detection of volume-changing metastatic brain tumors with the Jacobian operator field. Receiver operating characteristic analysis was used to assess the detection accuracy of the algorithm, and it was verified with jackknife resampling. The reference standard was based on detections by a neuroradiologist. The areas under the receiver operating characteristic curves were 0.925 for 1.5T and 0.965 for 3T. Furthermore, at its optimal performance, the algorithm achieved a sensitivity of 85.1% and 92.1% and specificity of 86.7% and 91.3% for 1.5T and 3T, respectively. Vessels were responsible for most false-positives. Newly developed or resolved metastatic brain tumors were a major source of false-negatives. The proposed algorithm could detect volume-changing metastatic brain tumors on longitudinal brain MRIs with statistically high accuracy, demonstrating its potential as a computer-aided change-detection tool for complementing the performance of radiologists, decreasing inter- and intraobserver variability, and improving efficacy.