Contrast Enhancement for Liver Tumor Identification

Abstract

In CT images, tumors located in a liver are generally identified by intensity difference between tumor and liver. The intensity of the tumor can be lower and or higher than that of the liver. However, the main problem of liver tumor detection from is related to low contrast between tumor and liver intensities. Tumor sometimes presents in a very small dimension and makes the detection even more difficult. In this work, we focus on contrast enhancement of CT images containing liver and tumor based on the histogram processing as a necessary preprocessing for liver tumor identification. Results show that using our proposed method, the contrast of the CT images can be enhanced and results in relatively accurate identification of tumors in the liver.

Atom search‐Jaya‐based deep recurrent neural network for liver cancer detection

“…Here h t n (g + 1) represents the position of the nth atom at the (g + 1)th iteration, rand t n denotes the random number, z t n (g) indicates the acceleration, h t wst,n represents the value for worst candidate, and m t 2n and m t 1n are the random number that lie in the interval [0, 1]. The equation (19) is the standard equation of the proposed AS algorithm, which is obtained by modifying the ASO with the Jaya optimization. By integrating the ASO with the Jaya provides more beneficial result in liver cancer detection with less computation cost and time.…”

“…The detection of liver cancer from the CT images poses a challenging issue due to less contrast among the liver and tumor. However, the presence of other human parts with various dimensions and equivalent intensity level makes the detection process more difficult [19]. Due to the complexity of liver anatomy issues and the insufficiency of organ shape, the accurate segmentation remains a difficult process.…”

“…Optimization algorithms are used to train DNN [23]. However, the deep learning methods are categorized into sparse autoencoder, deep belief network (DBN), restricted Boltzmann machine (RBM), and CNN, respectively [19]. The deep learning model specifies the advanced structure in CNN.…”

“…The detection of liver and its lesion poses a challenging issue due to the variability of high anatomical shape and the facts regarding the contact of neighbouring pixel intensity [26]. There exist a number of difficulties in the analysis of automatic cancer detection that includes less image contrast among the tissue samples due to the differences in scan time and perfusion, parenchyma, and the behaviour of lesions in the contrast enhancement [19]. To extract the boundary region of liver based on the abdominal CT image poses a major challenge in the computer-aided diagnosis, as the boundary region is weak in liver cancer [21].…”

“…The images are pre-processed to enable the user identify the presence of a tumor through CT images. To detect the liver cancer at an early stage is a major advantage in preventing the liver cancer [18,19].…”