&lt;title&gt;Knowledge-based localization of hippocampus in human brain MRI&lt;/title&gt;

Soltanian‐Zadeh, Hamid; Siadat, Mohammad-Reza

doi:10.1117/12.348569

Cited by 5 publications

(3 citation statements)

References 12 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have incorporated a rule-based expert system proposed by Siadat and Soltanian-Zadeh 34,35 for localization of hippocampus. This system examined the 2D coronal slices of the brain dataset one by one and after thresholding each slice to create CSF, gray matter (GM) and white-matter (WM) maps, it tries to identify important anatomical landmarks such as ventricles and Sylvian fissure.…”

Section: A Rule-based System For Localization Of Hippocampusmentioning

confidence: 99%

“…These procedures are based on pixel-by-pixel search of the binary images and a set of if-then rules that are based on the a priori knowledge of the brain anatomy applied within an expert system scheme. The detail of the algorithm used can be read in 34,35 . In the rest of this paper, we discuss incorporation of the localization results in the 3D deformable model and other aspects of the hippocampus segmentation.…”

Section: A Rule-based System For Localization Of Hippocampusmentioning

confidence: 99%

“…We present a two-stage method for segmentation of the hippocampus. First a knowledge-based method for the localization of the hippocampus, which is developed by Siadat and Soltanian-Zadeh 34,35 will be used. In the second part, we incorporate a knowledge-base into the deformable model for accurate segmentation of the hippocampus.…”

mentioning

confidence: 99%

See 2 more Smart Citations

<title>Knowledge-based deformable surface model with application to segmentation of brain structures in MRI</title>

et al. 2001

Self Cite

View full text Add to dashboard Cite

We have developed a knowledge-based deformable surface for segmentation of medical images. This work has been done in the context of segmentation of hippocampus from brain MRI, due to its challenge and clinical importance. The model has a polyhedral discrete structure and is initialized automatically by analyzing brain MRI sliced by slice, and finding few landmark features at each slice using an expert system. The expert system decides on the presence of the hippocampus and its general location in each slice. The landmarks found are connected together by a triangulation method, to generate a closed initial surface. The surface deforms under defined internal and external force terms thereafter, to generate an accurate and reproducible boundary for the hippocampus. The anterior and posterior (AP) limits of the hippocampus is estimated by automatic analysis of the location of brain stem, and some of the features extracted in the initialization process. These data are combined together with a priori knowledge using Bayes method to estimate a probability density function (pdf) for the length of the structure in sagittal direction. The hippocampus AP limits are found by optimizing this pdf. The model is tested on real clinical data and the results show very good model performance 1 .

show abstract

Section: A Rule-based System For Localization Of Hippocampusmentioning

confidence: 99%

Section: A Rule-based System For Localization Of Hippocampusmentioning

confidence: 99%

See 1 more Smart Citation

<title>Knowledge-based deformable surface model with application to segmentation of brain structures in MRI</title>

et al. 2001

Self Cite

View full text Add to dashboard Cite

show abstract

Hippocampus

Soltanian‐Zadeh

Elisevich

2006

Wiley Encyclopedia of Biomedical Engineering

View full text Add to dashboard Cite

The hippocampus (HC), a discrete anatomical landmark of the mesial temporal lobe of the brain, is part of the limbic system of the brain and subserves both learning and memory. Several clinical pathological entities are affected by the HC so that its clinical investigation has been a focus of intense work. Its intrinsic connectivity with its feedforward and feedback circuitry underlies a complex organization that, when disturbed, can give rise to debilitating conditions such as epilepsy. The application of electroencephalographic and imaging methodologies to the study of this structure has provided a means of not only lateralizing the site of pathology but determining the nature of the condition.

show abstract

A discrete curvature-based deformable surface model with application to segmentation of volumetric images

Ghanei

Soltanian-Zadeh²

2002

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

In this paper, we present a new curvature-based three-dimensional (3-D) deformable surface model. The model deforms under defined force terms. Internal forces are calculated from local model curvature, using a robust method by a least-squares error (LSE) approximation to the Dupin indicatrix. External forces are calculated by applying a step expansion and restoration filter (SEF) to the image data. A solution for one of the most common problems associated with deformable models, self-cutting, has been proposed in this work. We use a principal axis analysis and reslicing of the deformable model, followed by triangulation of the slices, to remedy self-cutting. We use vertex resampling, multiresolution deformation, and refinement of the mesh grid to improve the quality of the model deformation, which leads to better results. Examples of the model application to different cases (simulation, magnetic resonance imaging (MRI), computerized tomography (CT), and ultrasound images) are presented, showing diversity and flexibility of the model.

show abstract

<title>Knowledge-based localization of hippocampus in human brain MRI</title>

Cited by 5 publications

References 12 publications

<title>Knowledge-based deformable surface model with application to segmentation of brain structures in MRI</title>

<title>Knowledge-based deformable surface model with application to segmentation of brain structures in MRI</title>

Hippocampus

A discrete curvature-based deformable surface model with application to segmentation of volumetric images

Contact Info

Product

Resources

About