Medical Image Segmentation for Liver Diseases: A Survey

Hassan, Tarek M.; Elmogy, Mohammed; Sallam, Elsayed A.

doi:10.5120/20856-3629

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…When using monocular RGB data, the challenge of detecting NOs can be related to an object detection/recognition problem in the field of computer vision. PO detection/recognition is an extensively developed field within computer vision [3], [2], [56], [57], [4], [58], [59]. Approaches to detect objects work based on what qualities a researcher chooses to define their object by.…”

Section: Computer Vision For Object Detectionmentioning

confidence: 99%

Classifying Negative Objects With Neural Networks

Hooper

2024

Preprint

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<p>The term <em>object</em> is generally only associated with something that can be sensed. However, the empty space between and around objects can also be considered as objects, which we will define as negative objects. While incredibly important in some instances, they have been neglected by the research community in comparison to their positive counterparts. Without a properly developed lexicon for them, negative objects are hard to discuss and describe. This thesis develops the starting point for a lexicon for negative objects, builds a publicly accessible dataset, and demonstrates that they can be identified within an image with the application of a machine learning algorithm. The neural network performs at an average precision of 74% when identifying a certain type of negative object (holes). This algorithm also shows promise in being able to differentiate between holes and tunnels.</p>

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Section: Computer Vision For Object Detectionmentioning

confidence: 99%

Classifying Negative Objects With Neural Networks

Hooper

2024

Preprint

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“…The most popular segmentation approaches are histogram-based methods, region-based methods, edgebased methods, model-based methods, watershed methods, fuzzy logic methods. As shown in Table 1, each of these approaches has its advantages and disadvantages in terms of applicability, suitability, performance, and computational cost [2,3].…”

Section: Introductionmentioning

confidence: 99%

CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm

Anter

Hassenian

2019

Artificial Intelligence in Medicine

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A B S T R A C TLiver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed. To increase the contrast of the liver CT images, the intensity values are adjusted and high frequencies are removed using histogram equalization and median filter approach. It is followed by transforming the CT image to NS domain, which is described using three subsets (percentage of truth T, the percentage of indeterminacy I, and percentage of falsity F). The obtained NS image is enhanced by adaptive threshold and morphological operators to focus on liver parenchyma. The enhanced NS image passed to a watershed algorithm for post-segmentation process and liver parenchyma is extracted using the connected component algorithm. Finally, the liver tumors are segmented from the segmented liver using fast fuzzy c-mean (FFCM). A quantitative analysis is carried out to evaluate segmentation results using six different indices. The results show that the overall accuracy offered by the employed neutrosophic sets is accurate, less time consuming, less sensitive to noise and performs better on non-uniform CT images.

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“…Segmentation is a term refers to the process of dividing the image into several identical areas in terms of characteristics and features of the image [1], [2] according to some criteria of similarity such as color, density, texture etc. [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Liver Segmentation from Abdominal MRI Images using Active Contours

Mohamed

Seada

Hamdy

et al. 2017

IJCA

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Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a system for the automatic segmentation of the liver from Magnetic Resonance Images (MRI). The system works without the need for setting manual seed points or setting a region of interest. Instead, the proposed system automatically detects and segments the liver through relying on its anatomical features for detection and using active contour for segmentation. The proposed segmentation system begins with localizing the liver or a part of it from a given MRI image using biggest components analysis. The extracted liver part is later used as a mask for full liver segmentation using active contour. The proposed system is fully automatic, works on different cases of MRI images (different sizes, healthy and abnormal liver). The detection and segmentation of the liver succeeded in 95% of the test cases acquired from different MRI imaging modalities.

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Medical Image Segmentation for Liver Diseases: A Survey

Abstract: With the recent advances in the field of artificial intelligence and information technology, the improvement in the interpretation of the medical images has contributed significantly to the early diagnosis of different diseases.

Cited by 6 publications

References 20 publications

Classifying Negative Objects With Neural Networks

Classifying Negative Objects With Neural Networks

CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm

Automatic Liver Segmentation from Abdominal MRI Images using Active Contours

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