Segmentation of radar images using a combined watershed and Fisher techniques

Belkhaoui, B.; Toumi, Abdelmalek; Khenchaf, Ali; Khalfallah, Ali; Bouhlel, Med Salim

doi:10.1109/setit.2012.6481948

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…We note the V s is the segmented volume and V GT the ground truth one. We define the True Positive(TP) as the degree of matching between the 3D mesh of V s and the V GT expressed in (5).…”

Section: Methodsmentioning

confidence: 99%

“…Rodrigues et al used it with an open software "Mevislab" to extract the liver from the CT exam with a 87% of accuracy. In [5], the author used the Fisher algorithm to improve the watershed algorithm for the radar images segmentation. Moreover, there are some intensity based techniques which detect the object according to the intensity repartition.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Non-rigid Registration for 3D Active Shape Liver Modeling

Trabelsi¹,

Cherni²,

Sellem³

2018

Adv. sci. technol. eng. syst. j.

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To avoid biopsies, doctors use non invasive medical techniques such as the computed tomography. Even that, the detection of the liver remains a big challenge because of the gray level and shape variations which depend on patients and acquisition modalites. In this work, we propose to create a 3D liver model in the training phase of 3D active shape model algorithm. This training model will be deformed according to any given 3D data for liver segmentation. The contribution of our work is the use of the Non-rigid registration with a B-spline registration on the training phase. We tested our method on an open access database ("3D-IRCADb") and on our database obtained from the radiology department of the National Oncology Institute of Tunis. Both data-sets showed the reliability of the method with an accuracy equal to 69.98% and 71.18% respectively for our database and "3D-IRCADb".

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“…We note the V s is the segmented volume and V GT the ground truth one. We define the True Positive(TP) as the degree of matching between the 3D mesh of V s and the V GT expressed in (5).…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Non-rigid Registration for 3D Active Shape Liver Modeling

Trabelsi¹,

Cherni²,

Sellem³

2018

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Tumor detection is the well known application. We find also image processing applications [25,21], classification [22,24], remote detection, artificial intelligence etc. The means of combination are multiple.…”

Section: Belief Function Theorymentioning

confidence: 99%

Credal Fusion of Classifications for Noisy and Uncertain Data

Karem

Dhibi

Atkinson

et al. 2017

IJECE

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This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems. We materialize our main idea by using belief function theory to do combination between classification and clustering. This theory treats very well imprecision and uncertainty linked to classification. Experimental results show that our approach has ability to significantly improve the quality of classification of generic database.

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“…The last colour space that we will adopt is the HSV space. This type coding codes the image in three matrices: H (Hue) codes perceived colour, S (saturation) represents the purity of colour and V (value) quantifies the amount of light colour [30]. This type of coding is widely used for the segmentation of colour images [31].…”

Section: Image Colour Space Representationsmentioning

confidence: 99%

REPro.JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images

Amri

Khalfallah

Lapayre

et al. 2017

The Imaging Science Journal

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Medical images are known for their huge volume which becomes a real problem for their archiving or transmission notably for telemedicine applications. In this context, we present a new method for medical image compression which combines image definition resizing and JPEG compression. We baptise this new protocol REPro.JPEG (reduction/expansion protocol combined with JPEG compression). At first, the image is reduced then compressed before its archiving or transmission. At last, the user or the receiver decompresses the image then enlarges it before its display. The obtain results prove that, at the same number of bits per pixel lower than 0.42, that REPRo.JPEG guarantees a better preservation of image quality compared to the JPEG compression for dermatological medical images. Besides, applying the REPRo.JPEG on these colour medical images is more efficient while using the HSV colour space compared to the use of RGB or YCbCr colour spaces.

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Segmentation of radar images using a combined watershed and Fisher techniques

Cited by 7 publications

References 14 publications

Non-rigid Registration for 3D Active Shape Liver Modeling

Non-rigid Registration for 3D Active Shape Liver Modeling

Credal Fusion of Classifications for Noisy and Uncertain Data

REPro.JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images

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