Morphological Segmentation of the Kidneys From Abdominal Ct Images

Belgherbi, Aicha; Ismahen, Hadjidj; Bessaid, Abdelhafid

doi:10.1142/s0219519414500730

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…The watershed method processes many advantages: the simplicity to apply it, the resulting boundaries form are closed and connected (Beucher and Meyer, 1993), the boundaries produced always close with real contours of objects and the union of all the regions forms the entire image region. Besides, the Watershed transform allows a complete image division (Belgherbi et al, 2014). However, among the most important disadvantage of the watershed transform, we noted over-segmentation (excessively for natural images), sensitivity to noise, and poor detection of thin structures (Bessaid et al, 2009).…”

Section: Detection Of Microcalcifications Using Watershed Transformmentioning

confidence: 83%

Microcalcifications segmentation from mammograms for breast cancer detection

Ismahan

Feroui

Belgherbi

et al. 2019

IJBET

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The presence of microcalcifications (MCs) in X-ray mammograms provides an important early sign of women breast cancer. However, their detection still remains very complex due to the diversity in shape, size, their distributions and to the low contrast between the cancerous areas and surrounding bright structures in mammograms. This paper presents an effective approach based on mathematical morphology for detection of MCs in digitised mammograms. The developed approach performs an initial step in order to extract the breast area and removing unwanted artefacts out of the mammogram. Subsequently, an enhancement process is applied to improve appearance and increase the contrast of images and to eliminate noise. Once the breast region has been found, a segmentation phase through morphological watershed is performed in order to detect MCs. The performance of our approach is evaluated using a total of 22 mammograms extracted from the MIAS mammographic database, showing the presence of MCs. The obtained results were compared with manual detection, marked by an expert mammographic radiologist. These results show that the system is very effective, especially in terms of sensitivity.

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Section: Detection Of Microcalcifications Using Watershed Transformmentioning

confidence: 83%

Microcalcifications segmentation from mammograms for breast cancer detection

Ismahan

Feroui

Belgherbi

et al. 2019

IJBET

Self Cite

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“…In [ 20 ] Hu et al proposed a method of kidney segmentation which is based on statistical conditional random fields framework. In [ 21 ] the method of kidney segmentation proposed by Belgherbi et al is based on the anatomical information and mathematical morphology tools used in the image processing filed. As we can see from Table 3 the accuracy, sensitivity and specificity of our methods is higher than the methods of ref.20 and ref.21.…”

Section: Results and Evaluationmentioning

confidence: 99%

Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm

et al. 2015

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BackgroundOrgan segmentation is an important step in computer-aided diagnosis and pathology detection. Accurate kidney segmentation in abdominal computed tomography (CT) sequences is an essential and crucial task for surgical planning and navigation in kidney tumor ablation. However, kidney segmentation in CT is a substantially challenging work because the intensity values of kidney parenchyma are similar to those of adjacent structures.ResultsIn this paper, a coarse-to-fine method was applied to segment kidney from CT images, which consists two stages including rough segmentation and refined segmentation. The rough segmentation is based on a kernel fuzzy C-means algorithm with spatial information (SKFCM) algorithm and the refined segmentation is implemented with improved GrowCut (IGC) algorithm. The SKFCM algorithm introduces a kernel function and spatial constraint into fuzzy c-means clustering (FCM) algorithm. The IGC algorithm makes good use of the continuity of CT sequences in space which can automatically generate the seed labels and improve the efficiency of segmentation. The experimental results performed on the whole dataset of abdominal CT images have shown that the proposed method is accurate and efficient. The method provides a sensitivity of 95.46% with specificity of 99.82% and performs better than other related methods.ConclusionsOur method achieves high accuracy in kidney segmentation and considerably reduces the time and labor required for contour delineation. In addition, the method can be expanded to 3D segmentation directly without modification.

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“…Aicha Belgherbi et al [7] tried a semi-automatic technique for the segmentation of kidneys. The procedure utilized depends on numerical morphology.…”

Section: Hardware Specificationmentioning

confidence: 99%

Kidney Tumour Segmentation

Sharma¹

2019

Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19

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Medical Image Segmentation is a challenging field in the area of Computer Vision.In this work Two deep learning models were explored namely U-Net and ENet.The reason to shortlist U-Net was it is suitable on a small data set and also originally designed for Biomedical Image segmentation. However when compared to ENet it is much slower. To speed up the process of Kidney Tumor segmentation , ENet was shortlisted and also experimented on the data set provided. ENet was very fast as compared to U-Net , However some visual representations of the predicted results have shown promising results in U-Net better then ENet.A classification model called as Xception Model was also considered right in the first phase , so as to shortlist those slices from the CT which have the presence of Kidney. So that the output obtained can be given as an input to semantic segmentation model. This would allows us to speed up the process of Kidney Tumor Segmentation.Finally performance parameter which was used for evaluation segmentation models was IOU.

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Morphological Segmentation of the Kidneys From Abdominal Ct Images

Cited by 15 publications

References 11 publications

Microcalcifications segmentation from mammograms for breast cancer detection

Microcalcifications segmentation from mammograms for breast cancer detection

Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm

Kidney Tumour Segmentation

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