Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.

Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation. It operates at a faster speed than current methods and can avoid halo effects by using the median operation. We then consider the dehazing effects in compression by investigating the coding artifacts and motion estimation in cases of applying any dehazing method before or after compression. We conclude that better dehazing performance with fewer artifacts and better coding efficiency is achieved when the dehazing is applied before compression. Simulations for Joint Photographers Expert Group images in addition to subjective and objective tests with H.264 compressed sequences validate our conclusion.

An Investigation of Dehazing Effects on Image and Video Coding

Six different types of image fusion algorithms based on
discrete cosine transform (DCT) were developed and their
performance was evaluated. Fusion performance is not good while
using the algorithms with block size less than 8x8 and also the block
size equivalent to the image size itself. DCTe and DCTmx based
image fusion algorithms performed well. These algorithms are very
simple and might be suitable for real time applications.

https://nal-ir.nal.res.in/10440/1/7_Discrete_Cosine_Transform_based_Image_Fusion_Techniques.pdf

Discrete Cosine Transform based Image Fusion Techniques

Multi sensor image fusion algorithm based on directional Discrete Cosine Transform (DDCT) - Principal Component Analysis (PCA) hybrid technique has been developed and evaluated The input images were divided into non-overlapping square blocks and the fusion process was carried out on the corresponding blocks The algorithm works in two stages In first stage, modes 0 to 8 were performed on images to be fused For each mode, the coefficients from the images to be fused are used in the fusion process The same procedure is repeated for other modes Three different fusion rules are used in fusion process viz, 1 Averaging the corresponding coefficients (DDCTav), 2 Choosing the corresponding frequency band with maximum energy (DDCTek) and 3 Choosing the corresponding coefficient with maximum absolute value (DDCTmx) between the images After this stage, there are eight fused images, one from each mode In second stage, these eight fused images are fused using PCA Performance of these algorithms were compared using fusion quality evaluation metrics such as root mean square error (RMSE), quality index (QI), spatial frequency and fusion quality index (FQI) It was concluded from the results that DDCTav performs poor and DDCTek performs slightly better than DDCTmx Moreover, DDCTek is computationally simple and easily implementable on target hardware Matlab code has been provided for better understanding

Hybrid DDCT-PCA based multi sensor image fusion

The purpose of image enhancement is to improve the perceptibility of information contained in an image. Since the human visual system tends to extract image structure, enhancing the structural features can improve perceived image quality. This paper proposes an object-based multilevel contrast stretching method to enhance image structure. The method first segments the image into its constitute objects, which are treated as image structural components, using morphological watersheds and region merging; then separately stretches the image contrast at interobject level and intra-object level in different ways. At interobject level, an approach of stretching between adjacent local extremes is used to adequately enlarge the local dynamic range of gray levels between objects. At intra-object level, the uniform linear stretching is used to enhance the textural features of objects while maintaining their homogeneity. Since the proposed method directly operates on the object, it can avoid introducing ringing, blocking or other false contouring artifacts in structural appearance; moreover, it can effectively suppress over emphasizing of noise and roughly preserve the overall brightness of the image. Experimental results show that the proposed method can produce enhanced images with more natural appearance in comparison with some classical methods.

Object-based multilevel contrast stretching method for image enhancement

In this paper a new algorithm is presented for image enhancement in the discrete cosine transform (DCT) domain. The algorithm is based on a novel contrast measure that is defined for each DCT coefficient. This algorithm can be applied to the enhancement of images compressed with JPEG and it is especially useful when it is applied to enhance the direction contrast of the images. Experimental results show the effectiveness of the proposed algorithm.

Q. Sun

Papers

A new contrast measure based image enhancement algorithm in the DCT domain