Lossless Microarray Image Compression using Region Based Predictors

Neekabadi, A.; Samavi, Shadrokh; Razavi, Seyed Ali; Karimi, Nader; Shirani, Shahram

doi:10.1109/icip.2007.4379164

Cited by 17 publications

(5 citation statements)

References 10 publications

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“…The proposed method could also be used for video sequences [13] for changing of frame sizes. Furthermore, in some biomedical images [14,15] the background of image could be eliminated by the proposed resizing method. [9] with object protection, (e) results of Mansfield et al [9] with object protection and edge cropping.…”

Section: Resultsmentioning

confidence: 99%

Image seam carving based on content importance and depth maps

Shafieyan

Karimi

Nasr-Esfahani

et al. 2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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In many occasions images are transferred from one device to another with a different display size. In such situations images need to be resized. Image seam carving algorithms attempt to change the size of images while preserving image salient contents with minimum distortion. For this purpose energy map production is an important factor that can help determining the important objects and reducing distortion. Existing methods have some limitations and disadvantage. Due to lack of proper energy map that take advantage of both saliency and depth these methods could not attain proper results. In this paper, we develop a seam carving method based on object importance map. By jointly considering gradient, saliency and depth maps, we develop an efficient energy map. Furthermore, we proposed an edge preservation algorithm to avoid passing of seams through salient pixels. By taking advantage of both object importance map and edge preservation, our method achieved better seam carving performance. Experimental results show that the proposed approach produces better images than previous depth-aware and content-aware seam carving methods. Moreover, our results show that we can maintain both geometrical structures and salient objects while reducing visual artifacts.

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Section: Resultsmentioning

confidence: 99%

Image seam carving based on content importance and depth maps

Shafieyan

Karimi

Nasr-Esfahani

et al. 2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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“…The proposed method could be applied to different applications such as vessel detection in angiograms [42], image compression [43][44][45][46], and image fusion [47]. These are applications that may not have large training datasets and the proposed method could be helpful.…”

Section: Discussionmentioning

confidence: 99%

Classification of diabetic retinopathy using unlabeled data and knowledge distillation

Abbasi

Hajabdollahi

Khadivi

et al. 2021

Artificial Intelligence in Medicine

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“…Furthermore, the intensity mask needs to be coded. We used the method presented in [20] for coding the mask. This is done by first applying a simple context modeling maximize 0 values of the mask and minimize 1 and 2 values.…”

Section: Proposed Methodsmentioning

confidence: 99%

Lossless compression of mammographic images with region‐based predictor selection

Karimi

Samavi

Mahmoodzadeh

et al. 2013

IEEJ Transactions Elec Engng

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One of the main tools for early diagnosis of breast cancer is digital mammography. These images require large storage space and are difficult to be transmitted over communication links. In this paper we propose a context‐based method for lossless compression of these images. Some modifications are performed to customize the activity level classification model (ALCM) predictor to work best in mammograms. The function of the modified predictor changes for different main regions of these images. Also, best qualities of two other predictors are exploited and the results of the fittest predictor are selected adaptively for the prediction of a pixel. Moreover, context modeling is used for a better categorization of the prediction errors. The proposed algorithm was tested using images from a well‐known database and the results were compared with two standard compression methods of lossless mode of JPEG2000 and JPEG‐LS. The proposed method was proved to produce better compression results than those of the standard algorithms. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Lossless Microarray Image Compression using Region Based Predictors

Cited by 17 publications

References 10 publications

Image seam carving based on content importance and depth maps

Image seam carving based on content importance and depth maps

Classification of diabetic retinopathy using unlabeled data and knowledge distillation

Lossless compression of mammographic images with region‐based predictor selection

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