Region Of Interest Essay

In the modern years, medical imaging has become a very important aspect of medical field since its origin in the 1970s Image processing has developed into an integral part of medical science ranging from PET scan to melanoma detection. Both the hardware and software required for Image processing have improved drastically resulting in today’s world where the medical professionals can recognise and diagnose thousands of diseases using this technology.

Chapter 1 is an introduction to the thesis and provides an overview of the relevant current context of medical image interpretation literature and background of topic.

Abstract— Nowadays, with the increase in the dreadful diseases, huge amount of database is produced in hospitals and are exponentially increasing day by day. Utilizing these medical images after efficient classification plays a major role in case based reasoning and supports in clinical decision making. Therefore, it is important to classify these images and access them accurately. A modality classifier helps to classify the medical images based on the modality. In our study, we analyzed spatial and spectral features of Magnetic Resonance (MR) images and Computer Tomography (CT) scan and also the fusion of these features is performed. It is found that these modalities have different characteristics which help in classification. Initially, these images are preprocessing using the Median filter and spatial and spectral features were extracted and feature fusion is performed. The

In this report I will be discussing the different file formats, compression techniques, image resolution and colour depth. I will be explaining the different purposes, then, after I have issued an in-depth explanation of image quality and file size I will be completing a final conclusion about the best ones to use for certain tasks.

A two dimensional digital image is a representation of an image as a set of digital values, called pixels. Each pixel is associated to a specific position X_ij in the image and has an intensity value d (X_ij ) with information about the color and intensity at the point〖 X〗_ij. An image of 8 bit is usually stored as a matrix with integer entries between 0 and 255, where 0 is the weakest intensity and 255 is the strongest. Gray scale images are composed of shades of gray. Each pixel has a single value that tells us intensity, varying from 0 corresponding to black and 255 corresponding to white. The RGB color model is one possible model in which to represent a color image. Shades of red, green and blue are combined create other colors. A pixel in color image then consists of three integer values between 0 and 255. The values define the intensity of red, green and blue respectively. It is possible to represent 2563 = 16777216 different colors in this

The Haar transform is based on the calculations of the averages (approximation co-efficient) and differences (detail co-efficient). Given two adjacent pixels a and b, the principle is to calculate the average and the difference . If a and b are similar, s will be similar to both and d will be small, i.e., require few bits to represent. This transform is reversible, since and and it can be written using matrix notation as

This test method have a better performance in video quality with time-varying compression system, in other word, it gives an entire assessment for a longer video sequence.

In the present world lot of multimedia data is exchanged on internet and the percent of image data in that is highest. So we need to securely transmit that and this can be achieved by Image encryption.

The chronicle of modern data compression begins in the late 1940’s with a method de-signed by Claude Shannon and Robert Fano, logically named Shannon-Fano coding. It takes the frequencies of the symbols in a file to represent the more recurrent ones using shorter coding. This was improved upon by one of Fano’s students, David Huffman, into an optimal version, and has obsoleted Shannon-Fano coding (except for historical pur-poses).

Unlike many other imaging applications where image quality get enhanced and nice visual interceptions giving from the human eye but in case of medical applications are limited, such as the generation of the device which can be interpreted as clinically interesting functions to achieve to need the best possible diagnosis is important that is in ultrasound medical images are crisp

In the first unit, they decomposed the image to be watermarked in to four dimensional modified DWT coefficients, by adding pseudo-random codes at the high and middle frequency bands of the DWT of an image. In the second unit, a key has been generated from LHLH frequency bands of the 4-Level DWT image and this key is watermarked in to the original gray image. In the third unit, for data compression we used bit plane slicing technique where the original gray image is sliced in to 8 planes and we used bit plane 3 to embed in to the key watermarked image. The embedded key watermarked image is transmitted and the key watermarks are extracted with

This process is carried out by pretty much every program dealing with images in modern computers, as without them, single images could take up multiple megabytes each, and result in easy clutter of data storage. This is further complicated with video, as it requires 25+ pictures to be evaluated each second, which could quickly overcome even some of the strongest home computers. Basic image compression acts as a simple touch-up to modern video, as it allows for a decent chunk of size to be easily cut down. On the contrary, however, it will significantly lower the quality of a video if used in excess, so it often acts as merely a supplement to other, more powerful compression techniques. This trade-off can be seen in the GIF (Graphics Interchange Format) standard4, which uses heavy intra-frame compression in order to save space, causing image quality to drop significantly.

In contrast to single images, the large video bandwidth means that long messages can be embeded in video. Speed is also an important trouble due to the huge quantities of facts that need to be processed. Except for video production (which takes place before distribution), digital video is typically stored and distributed in compressed form (e.g., MPEG). Hence, it is often desired that the marked, compressed video have to no longer require greater bandwidth than the unmarked, compressed video. This bit-price constraint can also be a trouble for unmarried photographs. Compressed-domain video watermarking is especially appealing. Operating at the compressed bit stream obviates the need for compute-extensive, time-eating decompression and recompression, such that the watermark can be embedded on the time of distribution or reception. An example of a compressed-domain video watermarking system is briefly described here; a more thorough treatment appears in. This gadget operates on video compressed the use of the MPEG-2 compression widespread. MPEG-2 uses block movement reimbursement (BMC) to suggest motion and block discrete cosine remodel (DCT) compression to explain the residual mistakes after BMC. This is referred to as a hybrid video compression machine. In BMC, decompressed picture blocks available to both the encoder and decoder are translated to

The model of illumination-reflectance can be utilized to design a frequency-domain method in enhancing the image’s appearance using the gray-level ranged compression and simultaneously contrasting the enhancements [54,65]. This model suggests that each pixel value f(x, y) can be reflected as the outcome of an illumination component i (x, y) and a

Compression is performed by a program that uses an algorithm on how to shrink the size of the data.