**1. Introduction**

In medical imaging, different modalities replicate different details of human organs and tissues. For example, Magnetic Resosance Imaging (MRI) provides low density soft tissues such as blood vessels, whereas Computed Tomography(CT) provides clear detail about bone tissue and also provides the reference for location of the lesion [1]. As it is known, dose reduction lowers the radiation exposure risks, but at the same time decreases the image quality. By its nature, CT involves larger radiation doses than the more common, conventional x-ray imaging procedures [2]. We briefly discuss the nature of CT scanning and its main clinical applications, both in symptomatic patients and, in the screening of asymptomatic patients. We focus on the increasing number of CT scans being obtained, the associated radiation doses, and the consequent cancer risks in adults and particularly in children [3]. Although the risks for any one person are not large, the increasing exposure to radiation in the population may be a public health issue in the future. The use of CT has increased rapidly since 1980's, according to recent surveys, it is showing that

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 correct and efficient.

**2.1 Histogram equalization**

from its neighbourhood region.

**2.2 Adaptive histogram equalization**

component with equal price.

**63**

a picture. Properties of Adaptive Histogram Equalisation:

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

1.The frequency of the histogram reflects only the pixels in the image of a certain grey level values but not reflects the position of each pixel.

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

Adaptive bar graph feat (AHE) may be a pc image process technique wont to improve the distinction in pictures. It differs from normal bar graph feat within the respect that the adaptive technique computes many histograms, every admire a definite section of the image, and uses them to spread the brightness values of the image [12]. In its simplest type, every element is remodeled supported the bar graph of a sq. close that element. The transformation functions derived from the bar graphs is precisely constant as for normal histogram feat. The transformation perform is proportional to the accumulative distribution function (CDF) of element values within the neighbourhood. Pixels close to the image boundary have to be compelled to be treated specially, as a result of their neighbourhood wouldn't lie fully at intervals the image [13]. It is so appropriate for rising the native distinction and enhancing the definitions of edges in every region of a picture. However, AHE contains a tendency to over amplify noise in comparatively homogeneous regions of

• The size of the neighbourhood region is a parameter of the method. It improves the contrast at smaller scales and reduces the contrast at larger scales.

neighbourhood. This permits Associate in Nursing economical implementation of hardware that may compare the middle component with all different pixels within the neighbourhood [3]. Associate in Nursing unnormalized result price may be computed by adding two for every component with a smaller price than the middle component, and adding one for every

• Due to the character of bar chart feat, the resultant price of a component underneath AHE is proportional to its rank among the pixels in its

2.Histogram of an image doesn't overlap each sub section of an image.

function transformation method based on histogram modification.

Characteristics of Histogram of a digital image:

*A Hybrid Image Fusion Algorithm for Medical Applications*

*DOI: http://dx.doi.org/10.5772/intechopen.96974*

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 improve the contrast.

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 data sets are compared with PCA and DWT applied to medical images by implementing in MATLAB.

### **2. Image enhancement**

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].
