**Domain-Specific Software Engineering Design for Diabetes Mellitus Study Through Gene and Retinopathy Analysis**

Hua Cao, Deyin Lu and Bahram Khoobehi *Louisiana State University, University of Mississippi Medical Center, LSU Eye Center, USA* 

### **1. Introduction**

446 Biomedical Science, Engineering and Technology

Loh, K.C. (2004). Pharmacology of Oral Anti-hyperglycaemic Agents & Insulin (Invited

Maiese, K.; Chong, Z.Z. & Shang, Y.C. (2007). Mechanistic insights into diabetes mellitus and oxidative stress. *Current Medicinal Chemistry*, Vol. 16, No. 16, pp. 1729-1738.

Nayak, J.; Bhat, P.S.; Acharya, U.R.; Lim, C.M. & Gupta, M.(2008). Automated identification

Naylor, C.D.; Sermer, M.; Chen, E. & Sykora, K.(1996). Cesarean delivery in relation to birth

Petersen, K.F.; Befroy, D.; Dufour, S.; Dziura, J.; Ariyan, C.; Rothman, D.L.; DiPietro, L.;

possible role in insulin resistance. *Science*, Vol. 300, No. 5622, pp. 1140-1142. Pincus, S.M.(1991). Approximate entropy as a measure of system complexity. *Proc National* 

Rahman, M.A.; Aziz, Z.; Acharya, U.R.; Tan, P.H.; Natarajan, K.; Ng, E.Y.K.; Law, C.;

Robertson, R.P. (2004). Chronic oxidative stress as central mechanism for glucose toxicity in

Robertson, R.P.; Harmon, J.; Tran, P.O.; Tanaka, Y. & Takahashi, H. (2003). Glucose toxicity

Rosenstien, M.; Colins, J.J. & De Luca, C.J.(1993). A practical method for calculating largest Lyapunov exponents from small data sets. *Physica D*, Volume 65, pp. 117-134. Tanaka, Y.; Tran, P.O.; Harmon, J. & Robertson, R.P. (2002). A role for glutathione

Task Force of the European Society of Cardiology and North American Society of Pacing

Yoshida, K.; Hirokawa, J.; Tagami, S.; Kawakami, Y.; Urata, Y. & Kondo, T. (1995). Weakened

glutathione synthesis and efflux. *Diabetologia*, Vol. 38, No. 2, pp. 201-210. Yun, W.L.; Acharya, U.R.; Venkatesh, Y.V.; Chee, C., Lim C.M. & Ng, E.Y.K.(2008).

images. *Information Sciences*, Volume 178, No. 1, pp.106-121.

of different stages of diabetic retinopathy using digital fundus images. *J Med Syst*,

weight and gestational glucose tolerance: pathophysiology or practice style? Toronto Tri-Hospital Gestational Diabetes Investigators. *JAMA,* Volume 275,

Cline, G.W. & Shulman, G.I. (2003). Mitochondrial dysfunction in the elderly:

Subramaniam, T. & Shuen, W.Y.(2006). Analysis of plantar pressure in diabetic Type 2 subjects with and without neuropathy. *Innov Technol Biol Med*, Volume 27,

pancreatic islet beta cells in diabetes. *Journal of Biological Chemistry*, Vol. 279, No. 41,

in β-cells: Type 2 diabetes, good radicals gone bad, and glutathione connection.

peroxidase in protecting pancreatic beta cells against oxidative stress in a model of glucose toxicity. *Proceedings of National Academy of Science (USA)*, Vol. 99, No.19, pp.

and electrophysiology.(1996). Heart Rate Variability: Standards of measurement, physiological interpretation and clinical use. *Eur Heart J,* Volume 17, pp.354-381. Van der Akker, T.J.; Koeleman, A.S.M.; Hogenhuis, L.A. & Rompelman, G.(1983). Heart-rate

variability and blood pressure oscillations in diabetics with autonomic neuropathy.

cellular scavenging activity against oxidative stress in diabetes mellitus: regulation of

Identification of different stages of diabetic retinopathy using retinal optical

Article). *Singapore Family Physician*, Volume 30, pp.16-20.

Mandelbrot, B.B.(1983). Geometry of Nature. Freeman San Francisco.

*Academic Science,* Volume 88, pp.2297-2301.

*Diabetes*, Vol. 52, No. 3, pp. 581-587.

*Automedica*, Volume 4, pp.201-208.

Volume 32, No. 2, pp. 107-115.

pp.1165.

No. 2, pp.46-55.

pp. 42351-42354.

12363-12368.

Software engineering designs and practices differ widely among various application domains. This chapter is concentrating on high performance software engineering design for bioinformatics and more specifically for diabetes mellitus study through gene and retinopathy analysis. Complex gene interaction study offers an effective control of blood glucose, blood pressure and lipids. Early detection of retinopathy is effective in minimizing the risk of irreversible vision loss and other long-term consequence associated with diabetes mellitus**.**

Type 2 diabetes mellitus is a disorder of glucose homeostasis involving complex gene and environmental interactions that are incompletely understood. Mammalian homologs of nematode sex determination genes have recently been implicated in glucose homeostasis and type 2 diabetes mellitus. The Fem1b knockout (Fem1b-KO) mice have been developed, with targeted inactivation of Fem1b, a homolog of the nematode fem-1 sex determination gene. It shows that the Fem1b-KO mice display abnormal glucose tolerance and that this is due predominantly to defective glucose-stimulated insulin secretion. Arginine-stimulated insulin secretion is also affected. These data implicate Fem1b in pancreatic islet function and insulin secretion, strengthening evidence that a genetic pathway homologous to nematode sex determination may be involved in glucose homeostasis and suggesting novel genes and processes as potential candidates in the pathogenesis of diabetes mellitus. In addition, this chapter is going to introduce basic gene analysis approaches that can be applied on diabetes mellitus study. These approaches include searching Genbank online database using BLAST, mapping DNA, locating genes, aligning different DNA or protein sequences, determining genotypes, and comparing nucleotide or amino acid sequences using global and local alignment algorithms. Fem1b gene, as an example, is going to be discussed with these basic gene analysis approaches.

Diabetic retinopathy is the leading cause of new cases of blindness among Americans aged 20 to 64 in both predominantly white and black populations [1]. Despite the recommendation for yearly eye examinations and efforts to achieve this, of the approximately 17 million Americans with diabetes, about 6 million nationwide remain undiagnosed and untreated, or not receiving annual eye examinations, which can lead to diabetic retinopathy [2].

Early indications of retinal blood vessel abnormalities and complications provide important indicators for clinical timely diagnosis and treatment of diabetes mellitus and eye disorders. The software engineering design tool facilitates increasing the number of annual diabetic screening eye examinations, thereby reducing the long time wait for diabetic patients to receive eye examinations. Common activities in software engineering approach for retinopathy include single or multi-modality retinal image registration, fusion, vessel pattern recognition, arteries & veins identification, and vessel diameter measurement. These methods play a major role in the development of better methods of diagnosing and treating diabetic retinopathy. Fusing the multi-modality retinal images, which usually requires intensive computational effort, is a very challenging problem because of the possible vast content change and non-uniform distributed intensities of the involved images.

This chapter is going to present a novel approach of retinal image fusion. Control points are detected at the vessel bifurcations using adaptive exploratory algorithm. Mutual-Pixel-Count (MPC) maximization based heuristic optimization adjusts the control points at the sub-pixel level. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. A refinement of the parameter set is obtained at the end of each loop, and finally an optimal fused image is generated at the end of the iteration. By locking the multi-modality retinal images into one single volume, the algorithm allows ophthalmologists to match the same eye over time to get a sense of disease progress and pinpoint surgical tools to increase accuracy and speed of the surgery. The new algorithm can be easily expanded to human or animals' 3D eye, brain, or body image registration and fusion.
