153,844 research outputs found
Segmentation of fetal 2D images with deep learning: a review
Image segmentation plays a vital role in
providing sustainable medical care in this evolving biomedical
image processing technology. Nowadays, it is considered one of
the most important research directions in the computer vision
field. Since the last decade, deep learning-based medical image
processing has become a research hotspot due to its exceptional
performance. In this paper, we present a review of different
deep learning techniques used to segment fetal 2D images.
First, we explain the basic ideas of each approach and then
thoroughly investigate the methods used for the segmentation
of fetal images. Secondly, the results and accuracy of different
approaches are also discussed. The dataset details used for
assessing the performance of the respective method are also
documented. Based on the review studies, the challenges and
future work are also pointed out at the end. As a result, it is
shown that deep learning techniques are very effective in the
segmentation of fetal 2D images.info:eu-repo/semantics/publishedVersio
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Machine Learning Techniques for Quantification of Knee Segmentation from MRI
© 2020 Sujeet More et al. Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system
A Review on MR Image Intensity Inhomogeneity Correction
Intensity inhomogeneity (IIH) is often encountered in MR imaging,
and a number of techniques have been devised to correct this
artifact. This paper attempts to review some of the recent
developments in the mathematical modeling of IIH field.
Low-frequency models are widely used, but they tend to corrupt the
low-frequency components of the tissue. Hypersurface models and
statistical models can be adaptive to the image and generally more
stable, but they are also generally more complex and consume more
computer memory and CPU time. They are often formulated together
with image segmentation within one framework and the overall
performance is highly dependent on the segmentation process.
Beside these three popular models, this paper also summarizes
other techniques based on different principles. In addition, the
issue of quantitative evaluation and comparative study are
discussed
A review on medical image segmentation: techniques and its efficiency
Image segmentation is the procedure of separating an image into significant areas based on similarity or heterogeneity measures and it is widely used in many fields that involve digital imaging including the medical field. Medical images from Computed Tomography, Magnetic Resonance Imaging and Mammogram require a proper segmentation technique to decompose the images into parts for further analysis. However, a standard methodology for any type of medical image segmentation is yet to be developed. The current image segmentation techniques and its efficiency will be evaluated in order to discover the technique that is most appropriate to be used for medical image segmentation. Researches carried out on image segmentation techniques between the periods of 2000 to 2016 are analysed and examined. This study specifically compares the techniques by analysing the performance of each algorithm on breast cancer modalities
Segmentation of images by color features: a survey
En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown
- …