scholarly journals Automatic segmentation of breast carcinomas from DCE-MRI using a Statistical Learning Algorithm

2012 ◽  
Author(s):  
J. Jayender ◽  
K. G. Vosburgh ◽  
E. Gombos ◽  
A. Ashraf ◽  
D. Kontos ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Breast Carcinomas ◽  
Dce Mri

scholarly journals NaturaSat—A Software Tool for Identification, Monitoring and Evaluation of Habitats by Remote Sensing Techniques

2021 ◽  
Vol 13 (17) ◽  
pp. 3381
Author(s):  
Karol Mikula ◽  
Mária Šibíková ◽  
Martin Ambroz ◽  
Michal Kollár ◽  
Aneta A. Ožvat ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Automatic Segmentation ◽  
European Space Agency ◽  
Software Tool ◽  
Monitoring And Evaluation ◽  
Field Work ◽  
Direct Access ◽  
Deep Learning Algorithm ◽  
Space Agency ◽  
Remote Sensing Techniques

The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such as the semi-automatic and automatic segmentation methods, and natural numerical networks, together with validated examples comparing field surveys and software outputs. The software is robust enough for field work researchers and stakeholders to accurately extract target units’ borders, even on the habitat level. The deep learning algorithm, developed for habitat classification within the NaturaSat software, can also be used in various research tasks or in nature conservation practices, such as identifying ecosystem services and conservation value. The exact maps of the habitats obtained within the project can improve many further vegetation and landscape ecology studies.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

scholarly journals Differentiation between Pure Mucinous Breast Carcinomas and Fibroadenomas with Strong High-Signal Intensity on T2-Weighted Images from Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Breast Care ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 32-37
Author(s):  
Ning Qu ◽  
Yahong Luo ◽  
Tao Yu ◽  
Huihui Yu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Signal Intensity ◽  
High Signal ◽  
Breast Carcinomas ◽  
Resonance Imaging ◽  
Enhancement Pattern ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Objective: This study aimed to identify characteristics that can differentiate between pure mucinous breast carcinomas (PMBCs) and fibroadenomas (FAs) with strong high-signal intensity on T2-weighted images (T2-SHi) from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: The DCE-MRI tumor characteristics were compared and analyzed between 35 PMBCs and 70 FAs with T2-SHi. Results: Multivariate analysis revealed that delayed enhancement pattern was the only significant independent predictor (p = 0.007). Conclusion: A delayed enhancement pattern is the most reliable characteristic for differentiating PMBCs from FAs with T2-SHi from DCE-MRI.

scholarly journals Statistical Learning Algorithm for in situ and invasive breast carcinoma segmentation

2013 ◽  
Vol 37 (4) ◽  
pp. 281-292 ◽  
Author(s):  
Jagadeesan Jayender ◽  
Eva Gombos ◽  
Sona Chikarmane ◽  
Donnette Dabydeen ◽  
Ferenc A. Jolesz ◽  
...  
Keyword(s):  
Breast Carcinoma ◽  
Statistical Learning ◽  
Learning Algorithm ◽  
Invasive Breast Carcinoma

scholarly journals The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jianning Wu ◽  
Bin Wu
Keyword(s):  
Statistical Learning ◽  
Learning Algorithm ◽  
Lower Limbs ◽  
Classification Method ◽  
Support Vector ◽  
Accurate Identification ◽  
Index Method ◽  
Gait Symmetry ◽  
Significant Difference ◽  
Gait Asymmetry

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

scholarly journals Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lijian Zhang ◽  
Guangfu Liu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Rate Distortion ◽  
Modeling Method ◽  
Experimental Results ◽  
Image Modeling ◽  
3D Image ◽  
Three Dimensional Model ◽  
Image Shape

Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.

Sign in / Sign up

Export Citation Format

Share Document