**Study on Low-Power Image Processing for Gastrointestinal Endoscopy**

Meng-Chun Lin *Department of Computer National Chengchi University Taiwan*

#### **1. Introduction**

242 VLSI Design

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**7. References** 

Gastrointestinal (GI) endoscopy has been popularly applied for the diagnosis of diseases of the alimentary canal including Crohn's Disease, Celiac disease and other malabsorption disorders, benign and malignant tumors of the small intestine, vascular disorders and medication related small bowel injury. There are two classes of GI endoscopy; wired active endoscopy and wireless passive capsule endoscopy. The wired active endoscopy can enable efficient diagnosis based on real images and biopsy samples; however, it causes discomfort for the patients to push flexible, relatively bulky cables into the digestive tube. To relief the patients' discomfort, wireless passive capsule endoscopes are being developed worldwide (1)-(6).

The capsule moves passively through the internal GI tract with the aid of peristalsis and transmits images of the intestine wirelessly. Developed by Given Imaging Ltd., the PillCam capsule is a state-of-the-art commercial wireless capsule endoscope product. The PillCam capsule transmits the GI images at a resolution of 256-by-256 8-bit pixels and the frame rate of 2 frames/sec (or fps). Because of its high mobility, it has been successfully utilized to diagnose diseases of the small intestine and alleviate the discomfort and pain of patients. However, based on clinical experience; the PillCam still has some drawbacks. First, the PillCam cannot control its heading and moving direction itself. This drawback may cause image oversights and overlook a disease. Second, the resolution of demosaicked image is still low, and some interesting spots may be unintentionally omitted. Therefore, the images will be severely distorted when physicians zoom images in for detailed diagnosis. The first drawback is the nature of passive endoscopy. Some papers have presented approaches for the autonomous moving function (7; 8; 21; 22; 25). Very few papers address solutions for the second drawback. Increasing resolution may alleviate the second problem; however, it will result in significant power consumption in RF transmitter. Hence, applying image compression is necessary for saving the power dissipation of RF transmitter (9)-(14), (23), (24), (26).

Our previous work (11) has presented an ultra-low-power image compressor for wireless capsule endoscope. It helps the endoscope to deliver a compressed 512-by-512 image, while the RF transmission rate is at 1 megabits ((<sup>256</sup> <sup>×</sup> <sup>256</sup> <sup>×</sup> <sup>2</sup> <sup>×</sup> <sup>8</sup>)/10242) per second. No any references can clearly define how much compression is allowed in capsule endoscope application. We define that the minimum compression rate is 75% according to two considerations for our capsule endoscope project. The first consideration is that the new

the task of smoothing or sharpening. Therefore, we need a flexible ROF hardware to arbitrarily select wanted rank values into the operation procedure of LUM filter and we have proposed an architecture based on a maskable memory for rank-order filtering. The maskable memory structure, called dual-cell random-access memory (DCRAM), is an extended SRAM structure with maskable registers and dual cells. This dissertation is the first literature using maskable memory to realize ROF. Driving by the generic rank-order filtering algorithm, the memory-based architecture features high degree of flexibility and regularity while the cost is low and the performance is high. This architecture can be applied for arbitrary ranks and a variety of ROF applications, including recursive and non-recursive algorithms. Except efficiently eliminating annoying impulsive noises and enhance sharpness for GI images, the

Study on Low-Power Image Processing for Gastrointestinal Endoscopy 245

Instead of applying state-of-the-art video compression techniques, we proposed a simplified image compression algorithm, called GICam. Traditional compression algorithms employ the YCbCr quantization to earn a good compression ratio while the visual distortion is minimized, based on the factors related to the sensitivity of the human visual system (HVS). However, for the sake of power saving, our compression rather uses the RGB quantization (15) to save the computation of demosaicking and color space transformation. As mentioned above, the advantage of applying RGB quantization is two-fold: saving the power dissipation on preprocessing steps and reducing the computing load of 2-D DCT and quantization. Moreover, to reduce the hardware cost and quantization power dissipation, we have modified the RGB quantization tables and the quantization multipliers are power of two's. In GICam, the Lempel-Ziv (LZ) coding (18) is employed for the entropy coding. The reason we adopted LZ coding as the entropy coding, is because the LZ encoding does not need look-up tables and complex computation. Thus, the LZ encoding consumes less power and uses smaller silicon size than the other candidates, such as the Huffman encoding and the arithmetic coding. The target compression performance of the GICam image compression is to reduce image size by at least 75%. To meet the specification, given the quantization tables, we exploited the cost-optimal LZ coding parameters to meet the compression ratio requirement by simulating

When comparing the proposed image compression with the traditional one in (11), the power consumption of GICam image compressor can save 98.2% because of the reduction of memory requirement. However, extending the utilization of battery life for a capsule endoscope remains an important issue. The memory access dissipates the most power in GICam image compression. Therefore, in order to achieve the target of extending the battery life, it is

In the modern color theory (16; 17), most color spaces in used today are oriented either toward hardware design or toward product applications. Among these color spaces, the

processing speed of ROF can also meet the real-time image applications.

**2.1 The review of GICam image compression algorithm**

with twelve tested endoscopic pictures shown in Fig.3.

necessary to consider how to efficiently reduce the memory access.

**2.2 Analysis of sharpness sensitivity in gastrointestinal images 2.2.1 The distributions of primary colors in the RGB color space**

**2. GICam image compressor**

image resolution (512-by-512) that is four times the one (256-by-256) of the PillCam can be an assistant to promote the diagnosis of diseases for doctors. The other one is that we do not significantly increase the power consumption for the RF circuit after increasing the image resolution from the sensor. Instead of applying state-of-the-art video compression techniques, we proposed a simplified image compression algorithm, called GICam, in which the memory size and computational load can be significantly reduced. The experimental results shows that the GICam image compressor only costs 31K gates at 2 frames per second, consumes 14.92 mW, and reduces the image size by at least 75% .

In applications of capsule endoscopy, it is imperative to consider the tradeoffs between battery life and performance. To further extend the battery life of a capsule endoscope, we herein present a subsample-based GICam image compressor, called GICam-II. The proposed compression technique is motivated by the reddish feature of GI image. We have previously proposed the GICam-II image compressor in paper (20). However, the color importance of primary colors in GI images has no quantitative analysis in detail because of limited pages. Therefore, in this paper, we completely propose a series of mathematical statistics to systematically analyze the color sensitivity in GI images from the RGB color space domain to the 2-D DCT spatial frequency domain in order to make up for a deficiency in our previous work (20). This paper also refines the experimental results to analyze the performance about the compression rate, the quality degradation and the ability of power saving individually.

As per the analysis of color sensitivity, the sensitivity of GI image sharpness to red component is at the same level as the sensitivity to green component. This result shows that the GI image is cardinal and different from the general image, whose sharpness sensitivity to the green component is much higher than the sharpness sensitivity to the red component. Because the GICam-II starts compressing the GI image from the Bayer-patterned image, the GICam-II technique subsamples the green component to make the weighting of red and green components the same. Besides, since the sharpness sensitivity to the blue component is as low as 7%, the blue component is down-sampled by four. As shown in experimental results, with the compression ratio as high as 4:1, the GICam-II can significantly save the power dissipation by 38.5% when compared with previous GICam work (11) and 98.95% when compared with JPEG compression, while the average PSNRY is 40.73 dB. The rest of the paper is organized as follows. Section II introduces fundamentals of GICam compression and briefs the previous GICam work. Section III presents the sensitivity analysis of GICam image and shows the importance of red component in GI image. In Section IV, the GICam-II compression will be described in details. Then, Section V illustrates the experimental results in terms of compression ratio, image quality and power consumption. Finally, Section VI concludes our contribution and merits of this work.

Except using novel ultra-low-power compression techniques to save the power dissipation of RF transmitter in high-resolution wireless gastrointestinal endoscope systems. How to efficiently eliminate annoying impulsive noise caused by a fault sensor and enhance the sharpness is necessary for gastrointestinal (GI) images in wired/wireless gastrointestinal endoscope systems. To overcome these problems, the LUM filter is the most suitable candidate because it simultaneously has the characteristics of smoothing and sharpening. In the operational procedure of LUM filter, the mainly operational core is the rank-order filtering (ROF) and the LUM filter itself needs to use different kind of rank values to accomplish the task of smoothing or sharpening. Therefore, we need a flexible ROF hardware to arbitrarily select wanted rank values into the operation procedure of LUM filter and we have proposed an architecture based on a maskable memory for rank-order filtering. The maskable memory structure, called dual-cell random-access memory (DCRAM), is an extended SRAM structure with maskable registers and dual cells. This dissertation is the first literature using maskable memory to realize ROF. Driving by the generic rank-order filtering algorithm, the memory-based architecture features high degree of flexibility and regularity while the cost is low and the performance is high. This architecture can be applied for arbitrary ranks and a variety of ROF applications, including recursive and non-recursive algorithms. Except efficiently eliminating annoying impulsive noises and enhance sharpness for GI images, the processing speed of ROF can also meet the real-time image applications.
