Fixed Range Block Segmentation and Classification for Fractal Image Compression of Satellite Imageries

Veenadevi, S.V.; Ananth, A.

doi:10.1109/ised.2014.58

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Figure (13) shows the accuracy percentage. Features can be extracted on the basis of the fractal dimension through differences in density and the complexity of the texture in the whole image and classified according to the three groups, which were by ( 4) and according to what is shown in Figure (19) and to estimate the correct classification rates. The results showed that 97.2% and 97.7%and 98% of the three groups could be achieved in the medical images used, and for the pathological conditions in the images.…”

Section: Results and Dissectionmentioning

confidence: 99%

Analysing and processing medical images with increased performance using fractal geometry

Kadhim¹

2022

PEN

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The research relied on the application of a series of steps to analyze medical images, and to basically achieve this goal, a set of techniques were made from both fractal engineering and tissue analysis by improving the studied image and then analyzing the studied image texture in the fractal dimension and propose a hybrid method for segmenting images of complex situations and structures based on the geometric patterns that are repeated and represented by the fractal filter (Hurst), which is one of the modern techniques used in the field of digital image processing. Using fractal methods, that is, a specific application through real fractal structures of medical images and measuring their fractal dimensions and in capturing the exact features based on the scale in dimensional fractions, where the accuracy rate reached ) 98%( in diagnosing pathological conditions with an error rate close to zero. Also, the coefficients of multiple fractals were calculated (α) ,with a threshold factor of (4.5), the texture is also classified based on the fractal algorithm and Gray-Level Co-Occurrence Matrices (GLCM) and according to the experimental results performed on the medical images, the classification method provides a classification rate of 95%. To increase the accuracy, the lacunarity was calculated in the healthy medical images by applying fractal theorem filters where the gap ratio was close to (1) in the lacunarity size. The results also showed that the decrease in the contrast of the image with the continuation of the smoothing process or the decrease in the intensity levels of the image causes a significant decrease in the contrast of the image, especially in the areas of the edges.

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Section: Results and Dissectionmentioning

confidence: 99%

Analysing and processing medical images with increased performance using fractal geometry

Kadhim¹

2022

PEN

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Comparison of Segmentation-Based Image Compression Using Threshold, Region Growing, and Edge Detection

Chaturvedi

Khatri

2022

IJSRCSEIT

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In the presenting paper we are dealing with to develop a lossless image compression (IC) method to utilize spatial redundancies inbuilt in image data which employs a best possible amount of segmentation information. To obtaining Multiscale segmentation we are using a earlier proposed transform which gives a tree-structured segmentation of the picture into regions which are identified by grayscale homogeneity. In the given proposed algor we have to shorten the tree to controlling the size and no of regions so that we can get a rate balance between the derived the coding gain and the operating cost inherent in coding the segmented data. Another uniqueness of the given proposed approach is that we are using an image model contain individual descriptions of the pixels lying close to the edges of a section and others lying in the center. In our results we can see that this proposed algorithm is providing better performance comparable to all the best available methods and it provides 15-20% better compression if we compare it with the JPEG lossless image compression standard for a enormous variety of images.

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Fixed Range Block Segmentation and Classification for Fractal Image Compression of Satellite Imageries

Cited by 2 publications

References 14 publications

Analysing and processing medical images with increased performance using fractal geometry

Analysing and processing medical images with increased performance using fractal geometry

Comparison of Segmentation-Based Image Compression Using Threshold, Region Growing, and Edge Detection

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