BioMed Research International

Volume 2016, Article ID 7478219, 11 pages

http://dx.doi.org/10.1155/2016/7478219

## HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

^{1}Software College of Northeastern University, Shenyang, Liaoning 110819, China^{2}Department of Radiology, Chinese PLA General Hospital, Shenyang 110015, China

Received 4 September 2016; Revised 18 November 2016; Accepted 8 December 2016

Academic Editor: Enzo Terreno

Copyright © 2016 Qingjiao Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

#### 1. Introduction

Pathological image is an important basis for computer aided diagnosis and is regarded as the gold standard in disease diagnosis. Cell segmentation and identification are critical steps in various medical diagnoses; it is difficult to acquire accurate cell segmentation results because of low contrast and noise of image. To address this issue, it is necessary to enhance pathological image before cell segmentation process. The enhancement of pathological image can improve image quality and contrast, and it can provide more objective and reliable data support for doctors. This is of great significance and strong application value as it improves detection efficiency and medical diagnostic accuracy, meanwhile, reducing manual diagnosis error and human costs.

There are a variety of image enhancement methods and frameworks to improve image quality. Traditional histogram equalization- (HE-) based methods [1–3] are widely used for image enhancement owing to their simplicity. Besides, many new models and algorithms are proposed to process image. Reference [4] proposed an efficient transformation-free approach for color image enhancement, which manipulates pixels value directly in source RGB color space. Reference [5] presented a color image enhancement method using daubechies wavelet transform and HIS color space. Reference [6] enhanced the pathological anatomy images based on superresolution to improve medical diagnosis. In recent years, the Retinex theory is widely used in the medical image processing [7]. Many algorithms based on Retinex theory such as single-scale Retinex (SSR) [8], multiscale Retinex (MSR) [9], McCann99 [10], and Frackle-McCann [11] have been developed for image enhancement. There are also many other methods to enhance high dynamic range (HDR) images [12–14]. Reference [15] put forward a Bilateral Filtering-Dynamic Range Partitioning (BF-DRP) algorithm which can use bilateral filter (BF) on image to extract a coarse component and a details component which are processed independently and then recombined together to obtain final enhanced image. Reference [16] improved the BF-DRP algorithm by adding an adaptive Gaussian filter to smooth the imbalanced variation of gradient. Reference [17] proposed guided image filter (GIF) and [18] applied that in HDR image denoising and enhancement, which can avoid gradient flipping artifacts.

However, all these methods mainly target natural images or infrared images; the effect is unsatisfactory when they are used on pathological images. It remains a challenging task to obtain a good enhancement result for color pathological images because of three main issues: intensity inhomogeneity of image, detail discontinuity in tissue structures, and lower dynamic range of image. To address those issues, in this paper, we propose a HDR pathological image enhancement method based on GIF and improved bias field correction model. The main contributions of this paper are as follows. First, a new pathological image operational process is designed to denoise and enhance the source low dynamic range (LDR) image. Second, an improved bias field correction model is proposed to correct the intensity inhomogeneity and detail discontinuity of image. Third, a new method to generate HDR pathological image is presented, using LDR image and Haematoxylin (H) and Eosin (E) channel image after stain separation.

The remainder of the paper is organized as follows. In Section 2, we introduce the related work. Our proposed pathological image enhancement method is described in Section 3. Section 4 shows our experiments results and compares them with other enhancement methods. Finally, conclusions are summarized in Section 5.

#### 2. Related Work

##### 2.1. Bias Field Correction Model

The intensity inhomogeneity is a common phenomenon of medical images, which is attributed to many factors, such as nonuniform illumination, imaging equipment defect, and the complexity of human tissues. Intensity inhomogeneity is a critical factor that affects some image processing, because it will influence the true intensity region of different tissues and then lead to the errors of image segmentation or other image analysis processes. Therefore, it is one necessary step to remove the intensity inhomogeneity from the image. The bias field is a popular mathematical assumption of image intensity inhomogeneity, which is generally accepted at present, and it manifested as the smoothly varying of intensity within the same tissue of the image. This assumption can be represented by the following mathematical model [19]: where is the observed image with intensity inhomogeneity, is the true image, is the bias field, and is Gaussian noise with zero mean which can be ignored after denoising process and then get . There are usually two assumptions for the above model [20]:(1)The bias field is smoothly varying. That is, the bias field approximates a constant in a small neighborhood of every pixel in the observed image.(2)The true image describes the physical property of tissues and the value of this property should be the same in the same tissue. Thus we assume that the pixel value within every tissue of the true image is a constant.

The bias field correction procedure is used to remove the bias field from image and finally to obtain the corrected image . There are many bias field correction methods, and [20] proposed a new algorithm called multiplicative intrinsic component optimization (MICO) for bias field estimation, which achieved a good result. In that paper, the bias field is represented by a linear combination of a group of smooth basis functions as follows:where is a column vector valued function composed of basis functions, is the transpose operator, and is pixel point in the image, and is a column vector of the coefficients. Therefore, bias field estimation can be viewed as to find the optimal coefficients of the linear combination .

In addition, the true image is formulated as the following model:where is the constant of the th tissue and there are tissues in the image, is the percentage of the th tissue being in the pixel , there are for and for , and is the range of th tissue.

In order to calculate the bias field , that paper proposed an energy minimization function as follows:where is the whole image domain. After merging equations (2), (3), and (4), the energy function can be expressed asWe can see that the energy is the function about variables . The minimization of can be achieved by alternately solving one variable with the other two fixed. And we can finally obtain the bias field corrected image after solving the bias field estimation.

##### 2.2. Guided Image Filter

GIF filters the input image by considering the guidance image. It is a smoothing operator which can smooth filtering, preserve the edge details, and avoid the artifacts effectively. It is fast and easy to implement and can obtain a nice visual quality. GIF is derived from a local linear transformation model considering the content of a guidance image. The filtering process at a pixel can be formulated as follows [17]:where is the guidance image and the is the linear transform of in window centered at pixel . are the local linear coefficients, the calculating formula of which is as follows:where and are the mean value and variance separately of image in window , is the number of pixels within , is the input image, and is the mean of in window . is the regularization parameter also called smooth factor and used to prevent being too large.

However, a pixel is involved in more than one window that covers when computing (2) and has different value in different windows. Therefore, we can get the final value by averaging all and thus the output of the filter can be represented as follows:Here, the average local linear coefficients are and .

#### 3. Proposed Pathological Image Enhancement Method

As shown in Figure 1, the generic process of proposed pathological image enhancement method is introduced.