**2. Video processing in diagnosis helping system**

Since many years, the research about diagnosis helping devices is very active. This is true in both academic and industrial world. This can be explained by the fact that the possibilities of data analysis systems are becoming more and more complex and can extract a large amount of information. The helping in diagnosis can provide a solution to decrease the response time of the practitioner in urgent case or to help him in the preparation of the patient operation. This section firstly presents endocapsules as an example of one of the most constrained integrated diagnostic devices. It also representative of some of the major research domains in biomedical technology: image and signal processing, robotics and invivo communication. Next, the needs for diagnosis helping for such devices are presented, followed by an introduction about low level image processing in both consumers and medical components. Finally similarities between these two applications are developed and clues are given to appreciate required capacity for these embedded algorithms.

#### **2.1 Researches in diagnostic helping devices**

Researches in diagnostic helping devices cover a large number of domains, however one can focus on three items that emphasize the conception of an autonomous device. This is illustrated by Lab-On-Chips projects (Harada, 2008) that are able to do an auto diagnostic.

#### 1. The video processing:

4 Health Management – Different Approaches and Solutions

image processing with minor variations. Moreover, these systems should be updatable to

These requirements are also valid for large market devices such as cell phones and cameras. For example, general purposes or specific embedded processors are widely used like ARM microprocessors and Texas Instrument Digital Signal Processors (DSP) which are integrated into transportation, photonics, communications or entertainment (Texas Instrument, 2006). These markets drive both academic and industrial researches. The background knowledge is present inside laboratories; however its transfer to medical applications is not yet

This chapter provides clues to transfer consumers computing architecture approaches to the benefit of medical applications. The goal is to obtain fully integrated devices from diagnostic helping to autonomous lab on chip while taking into account medical domain specific

This expertise is structured as follows: the first part analyzes vision based medical applications in order to extract essentials processing blocks and to show the similarities between consumer's and medical vision based applications. The second part is devoted to the determination of elementary operators which are mostly needed in both domains. Computing capacities that are required by these operators and applications are compared to the state-of-the-art architectures in order to define an efficient algorithm-architecture adequation. Finally this part demonstrates that it's possible to use highly constrained computing architectures designed for consumers handled devices in application to medical domain. This is based on the example of a high definition (HD) video processing architecture designed to be integrated into smart phone or highly embedded components. This expertise paves the way for the industrialisation of intergraded autonomous diagnostic helping devices, by showing the feasibility of such systems. Their future use would also free the medical staff from many logistical constraints due the deployment of today's

Since many years, the research about diagnosis helping devices is very active. This is true in both academic and industrial world. This can be explained by the fact that the possibilities of data analysis systems are becoming more and more complex and can extract a large amount of information. The helping in diagnosis can provide a solution to decrease the response time of the practitioner in urgent case or to help him in the preparation of the patient operation. This section firstly presents endocapsules as an example of one of the most constrained integrated diagnostic devices. It also representative of some of the major research domains in biomedical technology: image and signal processing, robotics and invivo communication. Next, the needs for diagnosis helping for such devices are presented, followed by an introduction about low level image processing in both consumers and medical components. Finally similarities between these two applications are developed and

Researches in diagnostic helping devices cover a large number of domains, however one can focus on three items that emphasize the conception of an autonomous device. This is illustrated by Lab-On-Chips projects (Harada, 2008) that are able to do an auto diagnostic.

clues are given to appreciate required capacity for these embedded algorithms.

follow the science developments.

completely industrially ready.

constraints.

cumbersome systems.

**2. Video processing in diagnosis helping system** 

**2.1 Researches in diagnostic helping devices** 

In the case of endocapsule (Karargyris, 2010), the main goal of video processing is to analyse a video sequence in order to find different features like bleeding, polyps, tumours, etc. Theses kinds of diagnosis helping are usually done in two steps. First a camera equipped device grab images to diagnostic, these images are then transmitted through a wireless connection to a workstation that analyzes them during an off-line processing. Figure 2.1 depicts the PillCam by GivenImaging, and the Endocam by Olympus can also be cited;

Fig. 2.1. PillCam by GivenImaging

2. The mechanical systems for autonomous devices:

Some researches focus on the integration of mechanicals devices to endocapsules in order to give them the ability to surgery using micro-instrumentation such as biopsy. An example of such an endocapsule is "Miro'' (Kim and al., 2007) under Korea's Frontier 21 project as shown on Figure 2.2. The "Scuola Superiore Sant'Anna'' (Quirini, 2007) also tries to integrate small mechanic legs to a video-capsule in order to give the practitioner the ability to move freely in the intestinal system.

Fig. 2.2. Principe of the endocapsule ''Miro'' and prototypes of a mobile endo-capsule

#### 3. The communication and transfer protocol:

Communication protocol in human body is defined by the norm IEEE 820.15. Its frequency is 403 MHz. This is defined by the norm for in-vivo electronic devices. Antennas for this band are small while low emitting power is required due to limited loss of the signal in the environment. Moreover this frequency should not infers with usual communications devices. Energy efficiency is a critical point for the energetic life of an integrated and autonomous system. For this reason, many researchers work in order to find an optimal way to communicate between the device and the external world. There are three aspects of this research: the first one focuses on the silicon device technologies and materials. The second one focuses on the architecture trying to define the most efficient hardware architecture for

A Future for Integrated Diagnostic Helping 7

 Spectrography is a possible solution to define the nature of a tumor when the biopsy not easily feasible. Spectrography is based on the spectral response of the organic fabric

The importance of the consumer devices market pushes the academic and industrial labs to innovate. This is required by the integration of brand new features in order to create new products, while maintaining the production cost as low as possible. Most of these new features require high computing capacities while silicon area must be kept under control and power consumption need to be sustained as low as possible. First, silicon area has a direct impact on production cost; moreover, too large components may be incompatible with a product form factor. Power consumption has a direct impact on battery life, which is

For example, on 2010, cell phones' image sensor represented about 80% of the overall sensor market for about 5 000 millions Dollar . These sensors are systematically associated with a digital Image and Signal Processor (ISP) to reconstruct and enhance the images from raw format. Cell phone integrators need video module, which include a video sensor and ISP at a price of about one dollar. Lenses and sensor costs are reduced as most as possible by reduction of the matrix and pixel size (today 2µm pixel are the state of the art). In addition to traditional color image reconstruction from raw data, this pixel size reduction implies an image quality degradation that must be corrected using digital ISP. An example of traditional image correction and reconstruction pipeline is presented in Figure 2.3. However to keep production costs low, their silicon area must be maintained under a few square millimetres using today's technologies. This forbids the use of traditional image processing approaches such as the use of a frame memory which may require more than times of

Additional computing resources are used for high level application such as face recognition or augmented reality. Digital cameras and security cameras represent another part of the market of embedded image processing. Depending on their usage, they can embed low level to complex high level algorithms, from simple image enhancement to face recognition or

**2.3 Algorithms used for general image processing in consumer's devices and** 

to a laser operating at a specific wavelength (Péry, 2008).

**diagnostic helping** 

crucial for handled products.

silicon area budget.

**Denoising step 1**

**Raw image**

Raw

0,81 GOPs

motion detection and tracking.

**Denoising step 2**

Raw

1,02 GOPs

**Histogram enhancement**

Fig. 2.3. Example a of a low level image reconstruction video pipe.

Raw

0,5 GOPs

**White balancing**

Raw

**and multispectral measure**

0.84 GOPs

**Bilinear demosaïcing**

Raw

1,87 GOPs

**Edge enhancement**

G B

R

Total 2,12 GOPs

G B

**3D Reconstruction**

Total 2,12 GOPs

**RGB Video stream**

G B

R

R

embedded computing integration. Finally, the third one focuses on communication protocol, as well at hardware level – antennas, computing, power consumption – at system level – soft radio, compression, computing complexity reduction.

The most important part of required computing capacity is devoted to video processing. It is crucial for a diagnostic helping device such an endocapsule. The practitioners have to visualize the body exploration which requires large computing capacity to ensure a confortable real-time high resolution video. Video processing is also essential for the control of mechanical parts of endocapsules that enable movement or biopsy. For example its purpose is to extract features from the image for positioning. Consequently, the video processing block usually requires large silicon area on the component. For this reason this chapter will focus in the video processing part.
