Automatic medical image segmentation is one of the main tasks for many organs and pathology structure delineation. It is also a crucial technique in the posterior clinical examination of brain tumours, like applying radiotherapy or tumour restrictions. Various image segmentation techniques have been proposed and applied to different image types of images. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-net, for automatic brain tumour segmentation in multi-modal magnetic resonance images (MRI) based on deep learning. We introduced an input processing block to a customised fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brats brain tumour dataset collected in 2018 and achieved dice scores of 0.905,0.827, and 0.803 for the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) classes, respectively. This study also provides promising results compared to the traditional machine learning methods, such as support vector machines (SVM), random forest (RF) and other deep learning methods used in this context.
{"title":"PU-NET DEEP LEARNING ARCHITECTURE FOR GLIOMAS BRAIN TUMOUR SEGMENTATION IN MAGNETIC RESONANCE IMAGES","authors":"Yamina Azzi, Abdelouhab Moussaoui, Mohand-Tahar Kechadi","doi":"10.5566/ias.2879","DOIUrl":"https://doi.org/10.5566/ias.2879","url":null,"abstract":"Automatic medical image segmentation is one of the main tasks for many organs and pathology structure delineation. It is also a crucial technique in the posterior clinical examination of brain tumours, like applying radiotherapy or tumour restrictions. Various image segmentation techniques have been proposed and applied to different image types of images. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-net, for automatic brain tumour segmentation in multi-modal magnetic resonance images (MRI) based on deep learning. We introduced an input processing block to a customised fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brats brain tumour dataset collected in 2018 and achieved dice scores of 0.905,0.827, and 0.803 for the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) classes, respectively. This study also provides promising results compared to the traditional machine learning methods, such as support vector machines (SVM), random forest (RF) and other deep learning methods used in this context.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"99 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136017964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siamese network-based visual tracking algorithms have achieved excellent performance in recent years, but challenges such as fast target motion, shape and scale variations have made the tracking extremely difficult. The regression of anchor-free tracking has low computational complexity, strong real-time performance, and is suitable for visual tracking. Based on the anchor-free siamese tracking framework, this paper firstly introduces balance factors and modulation coefficients into the cross-entropy loss function to solve the classification inaccuracy caused by the imbalance between positive and negative samples as well as the imbalance between hard and easy samples during the training process, so that the model focuses more on the positive samples and the hard samples that make the major contribution to the training. Secondly, the intersection over union (IoU) loss function of the regression branch is improved, not only focusing on the IoU between the predicted box and the ground truth box, but also considering the aspect ratios of the two boxes and the minimum bounding box area that accommodate the two, which guides the generation of more accurate regression offsets. The overall loss of classification and regression is iteratively minimized and improves the accuracy and robustness of visual tracking. Experiments on four public datasets, OTB2015, VOT2016, UAV123 and GOT-10k, show that the proposed algorithm achieves the state-of-the-art performance.
基于Siamese网络的视觉跟踪算法近年来取得了很好的效果，但目标快速运动、形状和尺度变化等挑战使得跟踪非常困难。无锚跟踪回归计算复杂度低，实时性强，适合于视觉跟踪。基于无锚暹罗跟踪框架，本文首先在交叉熵损失函数中引入平衡因子和调制系数，解决训练过程中正负样本不平衡、难易样本不平衡造成的分类不准确问题，使模型更加关注对训练贡献较大的正样本和难样本。其次，改进回归分支的IoU (intersection over union)损失函数，不仅关注预测框与地面真值框之间的IoU，还考虑了两个框的宽高比和容纳两者的最小边界框面积，从而指导生成更精确的回归偏移量。迭代最小化了分类和回归的总体损失，提高了视觉跟踪的准确性和鲁棒性。在OTB2015、VOT2016、UAV123和GOT-10k四个公共数据集上的实验表明，该算法达到了最先进的性能。
{"title":"Sample-balanced and IoU-guided anchor-free visual tracking","authors":"Jueyu Zhu, Yu Qin, Kai Wang, Gao zhi Zeng","doi":"10.5566/ias.2929","DOIUrl":"https://doi.org/10.5566/ias.2929","url":null,"abstract":"Siamese network-based visual tracking algorithms have achieved excellent performance in recent years, but challenges such as fast target motion, shape and scale variations have made the tracking extremely difficult. The regression of anchor-free tracking has low computational complexity, strong real-time performance, and is suitable for visual tracking. Based on the anchor-free siamese tracking framework, this paper firstly introduces balance factors and modulation coefficients into the cross-entropy loss function to solve the classification inaccuracy caused by the imbalance between positive and negative samples as well as the imbalance between hard and easy samples during the training process, so that the model focuses more on the positive samples and the hard samples that make the major contribution to the training. Secondly, the intersection over union (IoU) loss function of the regression branch is improved, not only focusing on the IoU between the predicted box and the ground truth box, but also considering the aspect ratios of the two boxes and the minimum bounding box area that accommodate the two, which guides the generation of more accurate regression offsets. The overall loss of classification and regression is iteratively minimized and improves the accuracy and robustness of visual tracking. Experiments on four public datasets, OTB2015, VOT2016, UAV123 and GOT-10k, show that the proposed algorithm achieves the state-of-the-art performance.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"46 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135763452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Van der Jagt, Geurt Jongbloed, Martina Vittorietti
In various stereological problems an $n$-dimensional convex body is intersected with an $(n-1)$-dimensional Isotropic Uniformly Random (IUR) hyperplane. In this paper the cumulative distribution function associated with the $(n-1)$-dimensional volume of such a random section is studied. This distribution is also known as chord length distribution and cross section area distribution in the planar and spatial case respectively. For various classes of convex bodies it is shown that these distribution functions are absolutely continuous with respect to Lebesgue measure. A Monte Carlo simulation scheme is proposed for approximating the corresponding probability density functions.
{"title":"Existence and approximation of densities of chord length- and cross section area distributions","authors":"Thomas Van der Jagt, Geurt Jongbloed, Martina Vittorietti","doi":"10.5566/ias.2923","DOIUrl":"https://doi.org/10.5566/ias.2923","url":null,"abstract":"In various stereological problems an $n$-dimensional convex body is intersected with an $(n-1)$-dimensional Isotropic Uniformly Random (IUR) hyperplane. In this paper the cumulative distribution function associated with the $(n-1)$-dimensional volume of such a random section is studied. This distribution is also known as chord length distribution and cross section area distribution in the planar and spatial case respectively. For various classes of convex bodies it is shown that these distribution functions are absolutely continuous with respect to Lebesgue measure. A Monte Carlo simulation scheme is proposed for approximating the corresponding probability density functions.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"34 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136319960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, there are significant challenges in the classification, recognition, and detection of fruits and vegetables. An important step to solve this problem is to obtain an accurate segmentation of the object of interest. However, the background and object separation in a gray image shows high errors for some thresholding techniques due to uneven or poorly conditioned lighting. An accepted strategy to decrease the error segmentation is to select the channel of a RGB image with high contrast. This paper presents the results of an experimental procedure based on a binary segmentation enhancement by using the Otsu method. The procedure was carried out with images of real agricultural products with and without additional noise to corroborate the robustness of the proposed strategy. The experimental tests were performed by using our database of RGB images of agricultural products under uncontrolled illumination. The results exhibit that the best segmentation is based on the selection of the Blue channel of the RGB test images due to its better contrast. Here, the quantitative results are measured by applying the Jaccard and Dice metrics based on the ground-truth images as optimal reference. Most of the results show an average percentage improvement difference greater than 45.5% in two experimental tests.
{"title":"IMPROVEMENT PROCEDURE FOR IMAGE SEGMENTATION OF FRUITS AND VEGETABLES BASED ON THE OTSU METHOD.","authors":"Osbaldo Vite-Chávez, Jorge Flores-Troncoso, Reynel Olivera-Reyna, Jorge Ulises Munoz","doi":"10.5566/ias.2939","DOIUrl":"https://doi.org/10.5566/ias.2939","url":null,"abstract":"Currently, there are significant challenges in the classification, recognition, and detection of fruits and vegetables. An important step to solve this problem is to obtain an accurate segmentation of the object of interest. However, the background and object separation in a gray image shows high errors for some thresholding techniques due to uneven or poorly conditioned lighting. An accepted strategy to decrease the error segmentation is to select the channel of a RGB image with high contrast. This paper presents the results of an experimental procedure based on a binary segmentation enhancement by using the Otsu method. The procedure was carried out with images of real agricultural products with and without additional noise to corroborate the robustness of the proposed strategy. The experimental tests were performed by using our database of RGB images of agricultural products under uncontrolled illumination. The results exhibit that the best segmentation is based on the selection of the Blue channel of the RGB test images due to its better contrast. Here, the quantitative results are measured by applying the Jaccard and Dice metrics based on the ground-truth images as optimal reference. Most of the results show an average percentage improvement difference greater than 45.5% in two experimental tests.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134971598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, extant center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiply threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiply thresholds encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into the 3-bit MTCP. Furthermore, this paper proposes a completed multiply threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiply threshold encoding pattern achieves superior texture classification performance.
{"title":"A Completed Multiply Threshold Encoding Pattern for Texture Classification","authors":"Bin Li, Yibing Li, Q.M.Jonathan Wu","doi":"10.5566/ias.2824","DOIUrl":"https://doi.org/10.5566/ias.2824","url":null,"abstract":"The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, extant center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiply threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiply thresholds encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into the 3-bit MTCP. Furthermore, this paper proposes a completed multiply threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiply threshold encoding pattern achieves superior texture classification performance.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"54 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135464122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vesna Gotovac Đogaš, K. Helisova, B. Radović, J. Stanek, M. Zikmundová, Kateřina Brejchová
The paper concerns a new statistical method for assessing dissimilarity of two random sets based on one realisation of each of them. The method focuses on shapes of the components of the random sets, namely on the curvature of their boundaries together with the ratios of their perimeters and areas. Theoretical background is introduced and then, the method is described, justified by a simulation study and applied to real data of two different types of tissue - mammary cancer and mastopathy.
{"title":"Two-Step Method for Assessing Similarity of Random Sets","authors":"Vesna Gotovac Đogaš, K. Helisova, B. Radović, J. Stanek, M. Zikmundová, Kateřina Brejchová","doi":"10.5566/ias.2600","DOIUrl":"https://doi.org/10.5566/ias.2600","url":null,"abstract":"The paper concerns a new statistical method for assessing dissimilarity of two random sets based on one realisation of each of them. The method focuses on shapes of the components of the random sets, namely on the curvature of their boundaries together with the ratios of their perimeters and areas. Theoretical background is introduced and then, the method is described, justified by a simulation study and applied to real data of two different types of tissue - mammary cancer and mastopathy.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"206 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80419146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Handwritten numeral recognition has been an important area in the domain of pattern classification. The task becomes even more daunting when working with non-Roman numerals. While convolutional neural networks are the preferred choice for modeling the image data, the conception of techniques to obtain faster convergence and accurate results still poses an enigma to the researchers. In this paper, we present new methods for the initialization and the optimization of the traditional convolutional neural network architecture to obtain better results for Kannada numeral images. Specifically, we propose two different methods- an encoderdecoder setup for unsupervised training and weight initialization, and a particle swarm optimization strategy for choosing the ideal architecture configuration of the CNN. Unsupervised initial training of the architecture helps for a faster convergence owing to more task-suited weights as compared to random initialization while the optimization strategy is helpful to reduce the time required for the manual iterative approach of architecture selection. The proposed setup is trained on varying handwritten Kannada numerals. The proposed approaches are evaluated on two different datasets: a standard Dig-MNIST dataset and a custom-built dataset. Significant improvements across multiple performance metrics are observed in our proposed system over the traditional CNN training setup. The improvement in results makes a strong case for relying on such methods for faster and more accurate training and inference of digit classification, especially when working in the absence of transfer learning.
{"title":"Improved Model Configuration Strategies for Kannada Handwritten Numeral Recognition","authors":"G. D. Upadhye, U. Kulkarni, D. Mane","doi":"10.5566/ias.2586","DOIUrl":"https://doi.org/10.5566/ias.2586","url":null,"abstract":"Handwritten numeral recognition has been an important area in the domain of pattern classification. The task becomes even more daunting when working with non-Roman numerals. While convolutional neural networks are the preferred choice for modeling the image data, the conception of techniques to obtain faster convergence and accurate results still poses an enigma to the researchers. In this paper, we present new methods for the initialization and the optimization of the traditional convolutional neural network architecture to obtain better results for Kannada numeral images. Specifically, we propose two different methods- an encoderdecoder setup for unsupervised training and weight initialization, and a particle swarm optimization strategy for choosing the ideal architecture configuration of the CNN. Unsupervised initial training of the architecture helps for a faster convergence owing to more task-suited weights as compared to random initialization while the optimization strategy is helpful to reduce the time required for the manual iterative approach of architecture selection. The proposed setup is trained on varying handwritten Kannada numerals. The proposed approaches are evaluated on two different datasets: a standard Dig-MNIST dataset and a custom-built dataset. Significant improvements across multiple performance metrics are observed in our proposed system over the traditional CNN training setup. The improvement in results makes a strong case for relying on such methods for faster and more accurate training and inference of digit classification, especially when working in the absence of transfer learning.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"2 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85264895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Figliuzzi, Antoine Montaux-Lambert, F. Willot, Grégoire Naudin, Pierre Dupuis, B. Querleux, E. Huguet
Morphological models are commonly used to describe microstructures observed in heterogeneous materials. Usually, these models depend upon a set of parameters that must be chosen carefully to match experimental observations conducted on the microstructure. A common approach to perform the parameters determination is to try to minimize an objective function, usually taken to be the discrepancy between measurements computed on the simulations and on the experimental observations, respectively. In this article, we present a Bayesian approach for determining the parameters of morphological models, based upon the definition of a posterior distribution for the parameters. A Monte Carlo Markov Chains (MCMC) algorithm is then used to generate samples from the posterior distribution and to identify a set of optimal parameters. We show on several examples that the Bayesian approach allows us to properly identify the optimal parameters of distinct morphological models and to identify potential correlations between the parameters of the models.
{"title":"A Bayesian Approach to Morphological Models Characterization","authors":"B. Figliuzzi, Antoine Montaux-Lambert, F. Willot, Grégoire Naudin, Pierre Dupuis, B. Querleux, E. Huguet","doi":"10.5566/ias.2641","DOIUrl":"https://doi.org/10.5566/ias.2641","url":null,"abstract":"Morphological models are commonly used to describe microstructures observed in heterogeneous materials. Usually, these models depend upon a set of parameters that must be chosen carefully to match experimental observations conducted on the microstructure. A common approach to perform the parameters determination is to try to minimize an objective function, usually taken to be the discrepancy between measurements computed on the simulations and on the experimental observations, respectively. In this article, we present a Bayesian approach for determining the parameters of morphological models, based upon the definition of a posterior distribution for the parameters. A Monte Carlo Markov Chains (MCMC) algorithm is then used to generate samples from the posterior distribution and to identify a set of optimal parameters. We show on several examples that the Bayesian approach allows us to properly identify the optimal parameters of distinct morphological models and to identify potential correlations between the parameters of the models.","PeriodicalId":49062,"journal":{"name":"Image Analysis & Stereology","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73071814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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