Wavelet-domain hidden Markov tree (HMT) model has been recently proposed and applied to image processing, e.g., image segmentation. A new multiscale image segmentation method, called HMTseg, was proposed by Choi and Baraniuk using the waveletdomain HMT. In this paper, we study the HMTseg algorithm and investigate the Contextual Labeling Tree which is used for the context-based Bayesian intersca...

Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights in a principled way in inter-modality scenarios remains an open problem. Here we propose a label ...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

In this paper, a new object-based wavelet fusion technique is presented for the fusion of multispectral (MS) and panchromatic (PAN) images to improve spatial information and preserve spectral information. The basic idea is to build a segmented label image by statistical region merging and minimum heterogeneity rule (SRMMHR) segmentation method to guide the object-based image fusion (OBIF). Ther...

As an entry to the MICCAI 2012 Cardiac MR Right Ventricle Segmentation Challenge, this paper presents a multi-atlas-based automatic pipeline for segmenting the right ventricle in MR images. Multiatlas segmentation relies on two major components: image registration to propagate segmentation labels into target image that needs to be segmented, and label fusion to effectively combine those labels ...

Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task far more complicated than naively fusing semantic results. Prediction fusion therefore important to achieve accurate panoptic segmentation. In this paper, we present REFINE, pREdiction FusIon NEtwork segmentation, high-quality by improving cross-task ...

This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method ...

Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single fin...

14 Abstract—In this paper, we propose a new color image segmentation method based on a multilevel thresholding algorithm and data fusion techniques. We have revised the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions, and tested the performance of the new automatic thresholding method called the TSMO (Two-Stage Multi-level Thresholding) on the colo...

as the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. this processing comprises two fundamental and critical steps towar...