Image Processing in Medicine and Biology

*Artificial Intelligence - Applications in Medicine and Biology*

et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. The Journal of the American Medical Association. 2016;**316**:2402-2410.

[104] Mayo CS, Moran JM, Bosch W, Xiao Y, McNutt T, Popple R, et al. American Association of Physicists in Medicine Task Group 263: Standardizing nomenclatures in radiation oncology. International Journal of Radiation Oncology, Biology, Physics.

2018;**100**(4):1057-1066. DOI: 10.1016/j.

[105] Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary

view. Journal of Medical Internet Research. 2016;**18**(12):e323. DOI:

[106] Parodi S, Riccardi G, Castagnino N, Tortolina L, Maffei M, Zoppoli G, et al. Systems medicine in oncology: Signaling network modeling and new generation decision-support systems. Methods in Molecular Biology. 2016;**1386**:181-219. DOI: 10.1007/978-1-4939-3283-2\_10

[107] Memorial Sloan Kettering Cancer Center. Watson oncology. n.d. https:// www.mskcc.org/about/innovativecollaborations/watson-oncology [Accessed: 30 January 2019]

[108] Kohn MS, Sun J, Knoop S, Shabo A, Carmeli B, Sow D, et al. IBM's health analytics and clinical decision support. Yearbook of Medical Informatics. 2014;**9**:154-162. DOI: 10.15265/

[109] Bibault JE, Giraud P, Burgun A. Big data and machine learning in radiation oncology: State of the art and future prospects. Cancer Letters. 2016;**382**(1):110-117. DOI: 10.1016/j.

ijrobp.2017.12.013

10.2196/jmir.5870

IY-2014-0002

canlet.2016.05.033

DOI: 10.1001/jama.2016.17216

[96] Baumann M, Krause M, Overgaard J, Debus J, Bentzen SM, Daartz J, et al. Radiation oncology in the era of precision medicine. Nature Reviews. Cancer. 2016;**16**(4):234-249. DOI:

[97] Arimura H, Soufi M, Kamezawa H, Ninomiya K, Yamada M. Radiomics with artificial intelligence for precision medicine in radiation therapy. Journal of Radiation Research. 2018;**60**(1):150-157.

[98] Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, et al. Decoding tumour phenotype by noninvasive imaging using a

quantitative radiomics approach. Nature Communications. 2014;**5**:4006. DOI:

[99] Depeursinge A, Yanagawa M, Leung AN, Rubin DL. Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT. Medical Physics. 2015;**42**(4):2054-2063. DOI:

[100] Lambin P, Rios-Velazquez E,

DOI: 10.1016/j.ejca.2011.11.036

[101] Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. Journal of Radiation Research. 2018;**59**(suppl\_1): i25-i31. DOI: 10.1093/jrr/rrx102

10.1038/s41598-017-10649-8

[103] Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A,

[102] Lao J, Chen Y, Li ZC, Li Q, Zhang J, Liu J, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Scientific Reports. 2017;**7**:10353. DOI:

Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;**48**(4):441-446.

10.1038/nrc.2016.18

DOI: 10.1093/jrr/rry077

10.1038/ncomms5006

10.1118/1.4916088

**70**

**73**

**Chapter 4**

**Abstract**

is reviewed as well.

**1. Introduction**

rendering, scientific visualization

A Survey on 3D Ultrasound

Reconstruction Techniques

This book chapter aims to discuss the 3D ultrasound reconstruction and visualization. First, the various types of 3D ultrasound system are reviewed, such as mechanical, 2D array, position tracking-based freehand, and untrackedbased freehand. Second, the 3D ultrasound reconstruction technique or pipeline used by the current existing system, which includes the data acquisition, data preprocessing, reconstruction method and 3D visualization, is discussed. The reconstruction method and 3D visualization will be emphasized. The reconstruction method includes the pixel-based method, volume-based method, and function-based method, accompanied with their benefits and drawbacks. In the 3D visualization, methods such as multiplanar reformatting, volume rendering, and surface rendering are presented. Lastly, its application in the medical field

**Keywords:** ultrasound, 3D reconstruction, position tracking technology, volume

The medical imaging is very important for the physicians to visualize the inner anatomy of the patient for diagnosis and analysis purposes. There are various types of imaging modality, which are the magnetic resonance imaging (MRI), ultrasonography imaging, and computer tomography (CT) imaging. Recently, the use of ultrasound has become widely popular among the practitioners and researchers alike especially in the medical field, such as in obstetrics, in cardiology as well as in surgical guidance. This is due to the fact that the ultrasound is faster and safer, has

The conventional way to use the ultrasound machine is that the physician moves the ultrasound probe over the subject's skin to examine the region of interest (ROI). The ultrasound probe will feed the input signal to the ultrasound machine to display the 2D ultrasound image on the screen output. The 2D ultrasound image shows the cross-sectional part of the ROI. By using the hand-eye coordination approach, the physician is able to form a mentally constructed volume of that ROI for examination of the organ features and also to estimate the volume of the ROI. However, the reliance of 2D ultrasound images during the ultrasound scan-

noninvasive nature, and less expensive than the MRI and CT.

ning session can present some of the limitations as follows [1]:

*Farhan Mohamed and Chan Vei Siang*
