Guide to Medical Image Analysis

Toennies, Klaus D.

doi:10.1007/978-1-4471-2751-2

Cited by 42 publications

(17 citation statements)

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“…The sCT registration also gives the closest mean CT intensities to the truth, 15, 40, and 25 Hounsfield units for CSF, GM, and WM, respectively. 21 A visual inspection in Fig. 2 of the subject MRI shows the registration improvement near the ventricles (red arrow) and frontal gray matter (yellow arrow).…”

Section: Resultsmentioning

confidence: 98%

MR to CT registration of brains using image synthesis

et al. 2014

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Computed tomography (CT) is the standard imaging modality for patient dose calculation for radiation therapy. Magnetic resonance (MR) imaging (MRI) is used along with CT to identify brain structures due to its superior soft tissue contrast. Registration of MR and CT is necessary for accurate delineation of the tumor and other structures, and is critical in radiotherapy planning. Mutual information (MI) or its variants are typically used as a similarity metric to register MRI to CT. However, unlike CT, MRI intensity does not have an accepted calibrated intensity scale. Therefore, MI-based MR-CT registration may vary from scan to scan as MI depends on the joint histogram of the images. In this paper, we propose a fully automatic framework for MR-CT registration by synthesizing a synthetic CT image from MRI using a co-registered pair of MR and CT images as an atlas. Patches of the subject MRI are matched to the atlas and the synthetic CT patches are estimated in a probabilistic framework. The synthetic CT is registered to the original CT using a deformable registration and the computed deformation is applied to the MRI. In contrast to most existing methods, we do not need any manual intervention such as picking landmarks or regions of interests. The proposed method was validated on ten brain cancer patient cases, showing 25% improvement in MI and correlation between MR and CT images after registration compared to state-of-the-art registration methods.

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Section: Resultsmentioning

confidence: 98%

MR to CT registration of brains using image synthesis

et al. 2014

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“…Image enhancement is one of the helpful fields in medical applications due to more utilization of the images in the diagnosis purposes [11].…”

Section: Introductionmentioning

confidence: 99%

A Technique for Visual Quality Enhancement in Capsule Endoscopy Images Using Contrast Enhancement Algorithms

Khan¹

2016

IJRET

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“…The ultrasonography is a reliable method for patient examination, easy to apply, not expensive and having the possibility of repeatability. Other medical imaging techniques, such as the computer tomography (CT), or the magnetic resonance imaging (MRI) are irradiating or expensive [4]. The texture is an important property of the internal organ tissues, providing subtle information concerning the disease.…”

Section: Introductionmentioning

confidence: 99%

The role of the Textural Microstructure Cooccurrence Matrices in the classification of the abdominal tumors, based on ultrasound images

Mitrea

Nedevschi

Badea

2014

2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP)

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The automatic, non-invasive diagnosis of the abdominal tumors is an important issue in nowadays research. We develop computerized methods for this purpose, based on ultrasound images. We previously defined the textural model of these tumors, consisting of the relevant textural features that best characterize them and of their specific values. In this work, we developed a texture analysis method based on the Textural Microstructure Cooccurrence Matrix (TMCM) of order two and three, and we assessed its role in abdominal tumor recognition. We used feature selection methods in order to determine the most important features and to improve the classification process. We assessed the classification performance using the old and the newly resulted textural features. For the experiments, we considered the hepatocellular carcinoma (HCC), the most frequent malignant liver tumor, versus the cirrhotic parenchyma on which it evolved, as well as the colorectal tumors versus the Inflammatory Bowel Diseases (IBD).

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Guide to Medical Image Analysis

Cited by 42 publications

References 0 publications

MR to CT registration of brains using image synthesis

MR to CT registration of brains using image synthesis

A Technique for Visual Quality Enhancement in Capsule Endoscopy Images Using Contrast Enhancement Algorithms

The role of the Textural Microstructure Cooccurrence Matrices in the classification of the abdominal tumors, based on ultrasound images

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