Multimodal Registration of PET/MR Brain Images Based on Adaptive Mutual Information

Baâzaoui, Abir; Berrabah, Mouna; Barhoumi, Walid; Zagrouba, Ezzeddine

doi:10.1007/978-3-319-48680-2_32

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The hybrid method is a unified tool for mono-and multimodal 3D brain image registration. In fact, we extended the multi-modal 2D brain image registration work of [2]. The method is composed of five steps (Fig.…”

Section: Spm Statistical Parametric Mapping (Spmmentioning

confidence: 99%

Comparative Study of Relevant Methods for MRI/X Brain Image Registration

Abderrahim

Baâzaoui

Barhoumi

2020

Lecture Notes in Computer Science

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Several methods of brain image registration have been proposed in order to overcome the requirement of clinicians. In this paper, we assess the performance of a hybrid method for brain image registration against the most used standard registration tools. Most traditional registration tools use different methods for mono-and multi-modal registration, whereas the hybrid registration method is providing both mono and multi-modal brain registration of PET, MRI and CT images. To determine the appropriate registration method, we used two challenging brain image datasets as well as two evaluation metrics. Results show that the hybrid method outperforms all other standard registration tools and has achieved promising accuracy for MRI/X brain image registration.

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Section: Spm Statistical Parametric Mapping (Spmmentioning

confidence: 99%

Comparative Study of Relevant Methods for MRI/X Brain Image Registration

Abderrahim

Baâzaoui

Barhoumi

2020

Lecture Notes in Computer Science

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“…In fact, the first step aims to enhance the signal-to-noise ratio by eliminating unwanted parts in the background and smoothing the internal part of the region while preserving its borders. In fact, ADF allows to unblock high-frequency noise while preserving the main edges of structures [18]. Then, the classification step permits to define the target classes using K-means, before classifying them by the PNN [17].…”

Section: Segmentation Of the Midsagittal Slicementioning

confidence: 99%

Unsupervised Method Based on Superpixel Segmentation for Corpus Callosum Parcellation in MRI Scans

Jlassi

Barhoumi

Maktouf

2020

Lecture Notes in Computer Science

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In this paper, we introduce an unsupervised method for the parcellation of the Corpus Callosum (CC) from MRI images. Since there are no visible landmarks within the structure that explicit its parcels, non-geometric CC parcellation is a challenging task especially that almost of proposed methods are geometric or data-based. In fact, in order to subdivide the CC from brain sagittal MRI scans, we adopt the probabilistic neural network as a clustering technique. Then, we use a cluster validity measure based on the maximum entropy (Vmep) to obtain the optimal number of classes. After that, we obtain the isolated CC that we parcel automatically using SLIC (Simple Linear Iterative Clustering) as superpixel segmentation technique. The obtained results on two challenging public datasets prove the performance of the proposed method against geometric methods from the state of the art. Indeed, as best as we know, it is the first work that investigates the validation of a CC parcellation method on ground-truth datasets using many objective metrics.

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