51,755 research outputs found
Hybrid-Fusion Transformer for Multisequence MRI
Medical segmentation has grown exponentially through the advent of a fully
convolutional network (FCN), and we have now reached a turning point through
the success of Transformer. However, the different characteristics of the
modality have not been fully integrated into Transformer for medical
segmentation. In this work, we propose the novel hybrid fusion Transformer
(HFTrans) for multisequence MRI image segmentation. We take advantage of the
differences among multimodal MRI sequences and utilize the Transformer layers
to integrate the features extracted from each modality as well as the features
of the early fused modalities. We validate the effectiveness of our
hybrid-fusion method in three-dimensional (3D) medical segmentation.
Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the
proposed method outperforms previous state-of-the-art methods on the task of
brain tumor segmentation and brain structure segmentation.Comment: 10 pages, 4 figure
MRF-based image segmentation using Ant Colony System
In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality
A hybrid method for traumatic brain injury lesion segmentation
Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain
3D Bounding Box Estimation Using Deep Learning and Geometry
We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geometric constraints provided by a 2D object bounding box to produce a
complete 3D bounding box. The first network output estimates the 3D object
orientation using a novel hybrid discrete-continuous loss, which significantly
outperforms the L2 loss. The second output regresses the 3D object dimensions,
which have relatively little variance compared to alternatives and can often be
predicted for many object types. These estimates, combined with the geometric
constraints on translation imposed by the 2D bounding box, enable us to recover
a stable and accurate 3D object pose. We evaluate our method on the challenging
KITTI object detection benchmark both on the official metric of 3D orientation
estimation and also on the accuracy of the obtained 3D bounding boxes. Although
conceptually simple, our method outperforms more complex and computationally
expensive approaches that leverage semantic segmentation, instance level
segmentation and flat ground priors and sub-category detection. Our
discrete-continuous loss also produces state of the art results for 3D
viewpoint estimation on the Pascal 3D+ dataset.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods
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