Image Analysis & Stereology最新文献

PU-NET DEEP LEARNING ARCHITECTURE FOR GLIOMAS BRAIN TUMOUR SEGMENTATION IN MAGNETIC RESONANCE IMAGES 脑胶质瘤磁共振图像分割的Pu-net深度学习架构

4区计算机科学

Image Analysis & Stereology Pub Date : 2023-11-02 DOI: 10.5566/ias.2879

Yamina Azzi, Abdelouhab Moussaoui, Mohand-Tahar Kechadi

{"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}

引用次数: 0

Sample-balanced and IoU-guided anchor-free visual tracking 样本平衡和iou引导无锚视觉跟踪

4区计算机科学

Image Analysis & Stereology Pub Date : 2023-11-01 DOI: 10.5566/ias.2929

Jueyu Zhu, Yu Qin, Kai Wang, Gao zhi Zeng

{"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}

引用次数: 0

Existence and approximation of densities of chord length- and cross section area distributions 弦长和横截面积分布密度的存在和近似

4区计算机科学

Image Analysis & Stereology Pub Date : 2023-10-27 DOI: 10.5566/ias.2923

Thomas Van der Jagt, Geurt Jongbloed, Martina Vittorietti

引用次数: 1

IMPROVEMENT PROCEDURE FOR IMAGE SEGMENTATION OF FRUITS AND VEGETABLES BASED ON THE OTSU METHOD. 基于otsu方法的果蔬图像分割改进程序。

4区计算机科学

Image Analysis & Stereology Pub Date : 2023-10-26 DOI: 10.5566/ias.2939

Osbaldo Vite-Chávez, Jorge Flores-Troncoso, Reynel Olivera-Reyna, Jorge Ulises Munoz

{"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}

引用次数: 0

A Completed Multiply Threshold Encoding Pattern for Texture Classification 一种完整的纹理分类多重阈值编码模式

4区计算机科学

Image Analysis & Stereology Pub Date : 2023-10-22 DOI: 10.5566/ias.2824

Bin Li, Yibing Li, Q.M.Jonathan Wu

{"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}

引用次数: 0

Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part II 图像分析:第22届斯堪的纳维亚会议，SCIA 2023, Sirkka，芬兰，4月18-21日，2023，会议录，第二部分

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-31438-4

引用次数: 0

Image Analysis: 22nd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I. 图像分析:第22届斯堪的纳维亚会议，SCIA 2023, Sirkka，芬兰，4月18-21日，2023，会议录，第一部分。

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-31435-3

引用次数: 0

Two-Step Method for Assessing Similarity of Random Sets 随机集相似度评估的两步法

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-12-15 DOI: 10.5566/ias.2600

Vesna Gotovac Đogaš, K. Helisova, B. Radović, J. Stanek, M. Zikmundová, Kateřina Brejchová

引用次数: 0

Improved Model Configuration Strategies for Kannada Handwritten Numeral Recognition 改进的卡纳达语手写数字识别模型配置策略

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-12-15 DOI: 10.5566/ias.2586

G. D. Upadhye, U. Kulkarni, D. Mane

{"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}

引用次数: 0

A Bayesian Approach to Morphological Models Characterization 形态学模型表征的贝叶斯方法

IF 0.9 4区计算机科学

Image Analysis & Stereology Pub Date : 2021-12-15 DOI: 10.5566/ias.2641

B. Figliuzzi, Antoine Montaux-Lambert, F. Willot, Grégoire Naudin, Pierre Dupuis, B. Querleux, E. Huguet

引用次数: 0