**2.1 Histogram equalization**

more than 62 million CT scans are currently obtained ever year in the United States, as compared with about 3 million in 1980's. The largest use of CT scan, however, are within the classes of pediatric identification and adult screening, and these trends are often expected to continue for ensuing few years [4]. The rise in use of CT scan in kids has been driven primarily by the decrease within the time required to scan, that is a smaller amount than a second, and additionally eliminating the necessity for physiological condition to forestall the kid from moving throughout image acquisition method. The foremost growth space in exploitation CT scan for youngsters has been presurgical identification of inflammation, that CT seems to be each

The radiation doses from CT scanning are considerably larger than those from corresponding conventional radiography. Michael F. McNitt-Gray [3] discussed that the radiation doses to a particular organ from any given CT scan depends on number of factors, such as range of scans, the tube current and scanning time in milliampseconds (mAs), the scale of the patient, the axial scan vary, the scan pitch (the degree of overlap between adjacent CT slices), the tube voltage within the potential unit peaks (kVp), and therefore the specific style of the scanner being employed. Patient dosimetry and evaluation of image quality are basic aspects of any quality control program in diagnostic radiology. Image quality must be adequate for diagnosis and obtained with reasonable patient doses [5]. As per the recommendations of International Commission on Radiological Protection, No dose limit applies to medical exposure to patients, but diagnostic reference levels or reference values have been proposed by the International Commission on Radiologic Protection [6]. Thomas Lehnert et al. said that it is always the relative noise in CT images will increase as the radiation dose decreases, which means that there will always be a tradeoff between the need for low-noise images and the desirability of using low doses of radiation [4]. The low dose CT scan image usually suffers from serious noise and artifacts by using analytical reconstruction methods. It is always preferable to have standard imaging techniques that diminish the patient dose with reasonable image quality [7]. As part of implementation efforts, an important clinical requirement has been addressed that low-dose CT (LDCT) images need to be improved in the Electronic Health Records (EHR). Khalid et al., proposed an enhanced dynamic quadrant equalization for image contrast enhancement, in which input image histogram is divided into 8 subhistograms by using median values. For individual subhistograms, clipping of histogram is done by the average pixels. New dynamic range is assigned to each subhistograms and HE is done separately. This approach preserves the mean brightness [8]. As there is no guarantee that the contrast will always be increased by the histogram equalization [1], Adaptive Histogram Equalisation has been applied on low dose CT scan image to

This chapter gives a comparative study related to performance of the image fusion techniques. Organization of this paper is as follows; Section 2 explains the image enhancement techniques. The principle of PCA and DWT image fusion techniques are discussed in Section 3. In Section 4, fusion performance assessment techniques are explained. In Section 5, the results of fused images for two different

The goal of an image enhancement is to improve the visual effects of the entire image or to enhance the certain information in accordance with specific needs [9].

data sets are compared with PCA and DWT applied to medical images by

correct and efficient.

*Multimedia Information Retrieval*

improve the contrast.

implementing in MATLAB.

**2. Image enhancement**

**62**

Histogram equalization is a global processing technique used to spread the pixel values over the dynamic range of image and the equalized histogram must be approximately uniformly distributed in the dynamic range [10]. It is a distribution function transformation method based on histogram modification.

Characteristics of Histogram of a digital image:


It is not sure that the contrast will always be increased by the histogram equalization. There may be some cases in which histogram equalization can be worse. In that cases the distinction could also be decreased. In general, normal bar graph exploit uses an equivalent transformation that comes from the image bar graph to rework all pixels. This works well once the distribution of pel values is comparable throughout the image [11]. However, once the image contains regions that square measure considerably lighter or darker than most of the image, the distinction in those regions won't be sufficiently increased. Adaptive bar graph exploit (AHE) improves during this side by remodeling every pel with a change perform derived from its neighbourhood region.
