Background: Differentiation between malignant and benign vertebral compression fracture is often problematic. This is precisely difficult in elderly who are predisposed to benign compression caused by osteoporosis .Establishing correct diagnosis is of great importance in determining the treatment andprognosis.A study was performed to determine which magnetic resonance imaging findings are useful in discrimination between metastatic and acute osteoporotic compression fractures of the spine. Recently MRI is being increasingly used for evaluation of these fractures.Objectives: The aim of this study is to establish the correct diagnosis of malignant and benign compression vertebral fracture by MRI to determine treatment and prognosis.Methods: MRI of (45 )patients with vertebral compression fracture who underwent MRI of spine during 2 years period ( 2004-2006) in Neuro surgical hospital were retrospectively reviewed by an expedient radiologist , A collapsed vertebra were considered to be acute if there was recent history of back pain of less than 3moths. All MRI sequences needed for spine examination was done( T1WI SE, T2WI GE STIR, and Mylography ) and Gadolinium contrast medium was given to all patients..Results: Of total 45 patients (31male, 14 female, their age range between 18-72years) , The criteria used to differentiate between benign andmalignant collapse spine on MRI was based on the signal intensity, morphology and contrast enhancement for the correct diagnosis. Homogenous and diffuse abnormal signal intensity, posterior convexity and involvement of pedicles are sign that are strongly suggestive of malignant collapse .Conversely, a band like area of low signal intensity adjacent to depressed end plateand preservation of signal intensity of vertebra suggest benign nature of the collapse.Conclusions: We found that MRI features is helpful in differentiation of malignant and benign compression fractures in majority of cases, and if initial MRI finding are equivocal correlation with other images technique, follow up and biopsy in selected cases helpful in arriving correct diagnosis
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In this paper, we will present proposed enhance process of image compression by using RLE algorithm. This proposed yield to decrease the size of compressing image, but the original method used primarily for compressing a binary images [1].Which will yield increasing the size of an original image mostly when used for color images. The test of an enhanced algorithm is performed on sample consists of ten BMP 24-bit true color images, building an application by using visual basic 6.0 to show the size after and before compression process and computing the compression ratio for RLE and for the enhanced RLE algorithm.
There are many images you need to large Khoznah space With the continued evolution of storage technology for computers, there is a need nailed required to reduce Alkhoznip space for pictures and image compression in a good way, the conversion method Alamueja
In this paper, a method is proposed to increase the compression ratio for the color images by
dividing the image into non-overlapping blocks and applying different compression ratio for these
blocks depending on the importance information of the block. In the region that contain important
information the compression ratio is reduced to prevent loss of the information, while in the
smoothness region which has not important information, high compression ratio is used .The
proposed method shows better results when compared with classical methods(wavelet and DCT).
A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of th
... Show MoreFractal image compression gives some desirable properties like fast decoding image, and very good rate-distortion curves, but suffers from a high encoding time. In fractal image compression a partitioning of the image into ranges is required. In this work, we introduced good partitioning process by means of merge approach, since some ranges are connected to the others. This paper presents a method to reduce the encoding time of this technique by reducing the number of range blocks based on the computing the statistical measures between them . Experimental results on standard images show that the proposed method yields minimize (decrease) the encoding time and remain the quality results passable visually.