**1. Introduction**

102 Liver Tumors

Quaia E, Palumbo A, Rossi S, Degobbis F, Cernic S, Tona G, Cova M (2006). Comparison of

After Microbubble Contrast Injection. *AJR*; 186:1560–1570. ISSN 1007-9327 Sandulescu DL, Dumitrescu D, Rogoveanu I, Saftoiu A (2011). Hybrid ultrasound imaging

Skjoldbye B, Pedersen MH, Struckmann J, Burcharth F, Larsen T (2002). Improved detection

Solbiati L, Tonolini M, Cova L (2004). Monitoring RF ablation. *Eur Radiol*;14:34–42. print

Spârchez Z, Radu P, Anton O, Socaciu M, Badea R (2009). Contrast-Enhanced Ultrasound in

Carcinoma. *J Gastrointestin Liver Dis* Vol.18 No 2, 243-248. ISSN 1841-8724 Strobel D, Kleinecke C, Hänsler J, Frieser M, Händl T, Hahn EG, Bernatik T (2005). Contrast-

Tanaka S, Ioka T, Oshikawa O, Hamada Y, Yoshioka F (2001). Dynamic sonog- raphy of hepatic tumors. *Am J Roentgenol*;177:799–805. Online ISSN 1546-3141 Vilana R, Bianchi L, Varela M, Nicolau C, Sánchez M, Ayuso C, García M, Sala M, Llovet JM,

von Herbay A, Vogt C, Westendorff J, Häussinger D, Gregor M (2009). Correlation between

Weidener N, Semple JP, Welch WR, Folkman J (1991). Tumor angiogenesis and metastasis –

Wernecke K, Rummeny E, Bongartz G, Vassallo P, Kivelitz D, Wiesmann W, Peters PE,

Wu F, Wang ZB, Chen WZ, Zou JZ, Bai J, Zhu H, Li KQ, Jin CB, Xie FL, Su HB (2005).

Zapata E, Zubiaurre L, Castiella A, Salvador P, García-Bengoechea M, Esandi P, Arriola A,

therapeutic options? *Rev Esp Enferm Dig*; 102: 484-488. ISSN 1130-0108

http://www.nejm.org/doi/full/10.1056/NEJM199101033240101

*Roentgenol*; 157: 731-739. Online ISSN 1546-3141

235: 659-667. Print ISSN 0033-8419. Online ISSN 1527-1315

contrast agent infusion. *Ultrasound Med Biol*;28:439–444. ISSN 0301-5629 Solbiati L, Ierace T, Tonolini M, Cova L (2004). Guidance and monitoring of radiofrequency

(print) ISSN 2219-2840 (online)

ISSN 0938-7994 eISSN 1432-1084

0172-4614.

eISSN 1432-1084

550. ISSN 0172-4614.

S19-23. print ISSN 0938-7994 eISSN 1432-1084

Visual and Quantitative Analysis for Characterization of Insonated Liver Tumors

techniques (fusion imaging). *World J Gastroenterol*; 17(1):49-52. ISSN 1007-9327

and biopsy of solid liver lesions using pulse-inversion ultrasound scanning and

liver tumor ablation with contrast-enhanced ultrasound. *Eur J Radiol*; 51(Suppl):

Assessing Therapeutic Response in Ablative Treatments of Hepatocellular

enhanced sonography for the characterisation of hepatocellular carcinomascorrelation with histological differentiation. *Ultraschall in Med*;26:270–276. ISSN

Bruix J, Bru C; BCLC Group (2006). Is microbubble-enhanced ultrasonography sufficient for assessment of response to percutaneous treatment in patients with early hepatocellular carcinoma? *Eur Radiol*;16:2454–2462. print ISSN 0938-7994

SonoVue Enhancement in CEUS, HCC Differentiation and HCC Diameter: Analysis of 130 Patients with Hepatocellular Carcinoma (HCC). *Ultraschall in Med*; 30: 544–

correlation in invasive breast carcinoma. *N. Engl. J. Med*, 324, 1 – 7. Available from:

Reers B, Reiser M, Pircher W (1991). Detection of hepatic masses in patients with carcinoma: comparative sensitivities of sonography, CT, and MR imaging. *Am J* 

Advanced hepatocellular carcinoma: treatment with high-intensity focused ultrasound ablation combined with transcatheter arterial embolization. *Radiology*;

Beguiristain A, Ruiz I, Garmendia G, Orcolaga R, Alustiza JM. (2010). Are hepatocellular carcinoma surveillance programs effective at improving the Liver cancer is considered one of the major causes of death in humans [1]. Early detection of tumors is essential for increasing the survival chances of patients. Recent advancements in medical imaging modalities have enabled the acquisition of high-resolution CT datasets, and thus, allowing physicians to identify both small and large tumors by manual visual inspection. Owing to the large number of images in medical datasets, it is difficult to manually analyze all images, and useful diagnostic information may be overlooked. Moreover, the diagnoses are mainly based on the physician's subjective evaluation and are dependent on the physician's experience. Therefore, computer assisted diagnosis (CAD) and computer assisted surgery have become one of the major research subjects.

Until now, many methods have been proposed for tumor detection and segmentation in liver CT images. These methods can be classified as semi-automatic [2][3] and automatic [4][5]. Smeets *et al.* have proposed a semi-automatic level set method, which combines a spiral scanning technique with supervised fuzzy pixel classification [2]. Mala *et al.* employed wavelet-based texture features in order to train a neural network for use in tumor detection [4]. In the method proposed by Park *et al.* [5], the voxels representing the liver vessels are removed from liver images and a bimodal histogram is assumed for the intensity distribution of the liver and tumors. The optimal threshold to segment tumors is determined by a "mixture probability density" algorithm. In our previous study [6], we proposed tumor detection [7], which is a technique combining the expectation maximization algorithm and a three-dimensional region of interest (ROI) detection method. However, if the image contrast is low, it is difficult to accurately remove vessels from the image. All the above-mentioned methods can locate tumors that are sufficiently large and have distinct boundaries. Semiautomatic approaches for handling a large number of tumors would need extensive user interactions, and therefore are error prone and tedious.

<sup>\*</sup> Tomoko Tateyama1, Wei Xiong2, Jiayin Zhou2, Makoto Wakamiya3, Syuzo Kanasaki3,

Akira Furukawa3 and Yen Wei Chen1

*<sup>1</sup> Department of Science and Engineering, Ritsumeikan Univesity, Shiga, Japan,* 

*<sup>2</sup> Institute for Infocomm Research, Singapore,* 

*<sup>3</sup> Shiga University of Medical Science, Shiga, Japan.*

We propose a new method for detecting tumors in CT images. Our method is based on adaptive contrast enhancement and the expectation maximization / maximization of the posterior marginal (EM/MPM) algorithm. User interaction is not required and both large and small tumors can be accurately found. Compared with our previously reported method [6], the newly proposed method is also suitable for images with poor contrasts. We describe the method in Sections 2–6 and present the experimental results in Section 7, followed by our conclusions.
