Efficient Liver Segmentation Based on the Spine

Seo, Kyung-Sik; Ludeman, Lonnie C.; Park, Seungjin; Park, Jong-An

doi:10.1007/978-3-540-30198-1_41

Cited by 20 publications

(10 citation statements)

References 4 publications

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“…It can be observed that the values of grey levels from the neighbourhood of the coordinates P 21 = (xP 21 , yP 21 ) expressed by the matrix B belong to the interval (50, 255], i.e. they belong to the G 1 found in the anatomical area occupied by muscles and ribs.…”

Section: Stage 4 Finding Points Of Subsequent Polylinesmentioning

confidence: 99%

“…Publications [2,5] describe a technique consisting in detecting the liver contour before extracting it from the image (DBE-Detect Before Extract). The authors of publication [21] first segmented the lumbar section of the spine, and then determined the borders of regions of interest (ROI) in which the liver shape is found. The lumbar spine section is segmented using thresholding for specified grey-levels of the image and also employing a histogram transformation.…”

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confidence: 99%

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Automatic Liver Segmentation from 2D CT Images Using an Approximate Contour Model

Ciecholewski

2013

J Sign Process Syst

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Due to its precision, computed tomography (CT) is now generally used to image the liver and diagnose its diseases. Computer-assisted methods aimed at facilitating the extraction of organ shapes from medical images and helping to diagnose disease entities are rapidly developing. This study presents a new method of automatically segmenting the shape of the liver, both for cases free of lesions and those showing certain disease units, i.e. focused lesions like hemangiomas and hepatomas. For the 1,330 2D CT images of the abdominal cavity and liver analysed, the Dices similarity coefficient amounted to 81.3 %. The method proposed is to be used in an IT system supporting liver diagnostics.

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Section: Stage 4 Finding Points Of Subsequent Polylinesmentioning

confidence: 99%

mentioning

confidence: 99%

Automatic Liver Segmentation from 2D CT Images Using an Approximate Contour Model

Ciecholewski

2013

J Sign Process Syst

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“…Susomboon et al [26] presented texture features to perform region classification for extracting liver's soft tissue. Seo et al [27] employed a multimodal threshold method based on piecewise linear interpolation that used spine location as a reference point. Forouzan et al [28] introduced a multilayer threshold technique, in which by statistical analysis of the liver intensity it calculates the threshold value.…”

Section: Computed Tomography (Ct)mentioning

confidence: 99%

Analysis of Watermarking Techniques for Medical Images Preserving ROI

Rathi¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Telemedicine is a well-known application, where enormous amount of medical data need to be securely transfer over the public network and manipulate effectively. Medical image watermarking is an appropriate method used for enhancing security and authentication of medical data, which is crucial and used for further diagnosis and reference. This paper discusses the available medical image watermarking methods for protecting and authenticating medical data. The paper focuses on algorithms for application of watermarking technique on Region of Non Interest (RONI) of the medical image preserving Region of Interest (ROI). The medical images can be transferred securely by embedding watermarks in RONI allowingverification of the legitimate changes at the receiving end without affecting ROI.

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“…relative to a fixed frame of reference. It makes sense to search for (say) kidneys in regions specified relative to a fixed point such as the spine [4,8]; it makes far less sense to search for them in regions specified purely in image coordinates, which have little anatomical relevance.…”

Section: Introductionmentioning

confidence: 99%

Automatic spine identification in abdominal CT slices using image partition forests

Golodetz

Voiculescu

Cameron

2009

2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis

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The identification of key features (e.g. organs and tumours) in medical scans (CT, MRI, etc.) is a vital first step in many other image analysis applications, but it is by no means easy to identify such features automatically. Using statistical properties of image regions alone, it is not always possible to distinguish between different features with overlapping greyscale distributions. To do so, it helps to make use of additional knowledge that may have been acquired (e.g. from a medic) about a patient's anatomy. One important form this external knowledge can take is localization information: this allows a program to narrow down its search to a particular region of the image, or to decide how likely a feature candidate is to be correct (e.g. it would be worrisome were the aorta identified as running through the middle of a kidney). To make use of this information, however, it is necessary to identify a suitable frame of reference in which it can be specified. This frame should ideally be based on rigid structures, e.g. the spine and ribs. In this paper, we present a method for automatically identifying the spine in image partition forests of abdominal CT slices as a first step towards defining a robust coordinate system for localization.

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Efficient Liver Segmentation Based on the Spine

Cited by 20 publications

References 4 publications

Automatic Liver Segmentation from 2D CT Images Using an Approximate Contour Model

Automatic Liver Segmentation from 2D CT Images Using an Approximate Contour Model

Analysis of Watermarking Techniques for Medical Images Preserving ROI

Automatic spine identification in abdominal CT slices using image partition forests

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