Digital rectal examination in a simulated environment using sweeping palpation and mechanical localization

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 1, pp. 169-175 JANUARY 2014 / 169

© KSPE and Springer 2014

Digital Rectal Examination in a Simulated Environmentusing Sweeping Palpation and Mechanical Localization

Yeongjin Kim1, Bummo Ahn2, Youngjin Na1, Taeyoung Shin3, Koonho Rha3, and Jung Kim1,#

1 Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, 305-7012 The Simulation Group, Center for Integration of Medicine and Innovative Technology, Department of Radiology, Harvard Medical School, Cambridge, USA

3 Department of Urology, Yonsei University College of Medicine, Urological Science Institute, Seoul, Korea# Corresponding Author / E-mail: [email protected], TEL: +82-42-350-3231, FAX: +82-42-350-5230

KEYWORDS: Prostate cancer, Mechanical diagnosis, Mechanical property map

Computerized palpation systems have been studied for the quantitative characterization of prostate properties. The aim of this study

was to evaluate the reliability of mechanical tumor localization maps by estimating correlation with pathological maps. A total of 120

indentations were performed on 10 specimens by using a sweeping palpation system in simulated environment conditions. Suspicious

tumor lesions from the mechanical localization were compared to those of the pathological maps. The concordance rate between the

mechanical localization maps and pathological maps was 81.7% (98/120). The positive predictive value (PPV) and the negative

predictive value (NPV) of the proposed localization system were 78.6% (59/75) and 86.7% (39/45), respectively. Based on these data,

the suspicious tumor lesions of mechanical localization maps were in close agreement with those from the pathological maps. The

findings suggest that the high compatibility and detection rate of prostate tumor could be enhanced if computerized palpation and

the conventional diagnosis are used synergistically. This study may contribute to technological progress in overcoming diagnostic

limitations, which include many complications from digital rectal examination (DRE) and transrectal ultrasound (TRUS) guided

needle biopsy.

Manuscript received: May 24, 2013 / Accepted: November 26, 2013

1. Introduction

Prostate cancer is the most common cancer in men over 50, and

more men will die due to prostate cancer than from any other cancer

except lung cancer. To detect prostate cancer, physicians typically have

used digital rectal examination (DRE), a serum prostate specific

antigen test (PSA test), and finally, transrectal ultrasound (TRUS)-

guided biopsy.1-5 However, a PSA test alone is not a reliable method for

detecting prostate cancer.6 It has been reported that the positive

predictive value (PPV) for DRE alone is as low as 28.0%,7 and even

in cases of men with abnormal PSA levels, the PPV of DRE is up to

33%8 The cancer detection rate of the biopsy is approximately 25%,9

and it has side effects after the biopsy, such as pain, post-biopsy

infection, hematuria, hematochezia, hematospermia, vasovagal reflex,

voiding difficulty and acute urinary retention.10 Fortunately, with the

combination of DRE, PSA, as well as family and personal history

tracking, many studies have shown that fewer men need to undergo the

biopsy to detect clinically prostate cancer (that is, the early detection

rate of prostate cancer can be enhanced if DRE, the PSA test, and the

history tracking are used synergistically).11-14 DRE has advantages such

as the prompt availability, cost effectiveness, and low risk. However,

compared to other tests, its results are not objective or quantitative

enough to constitute a primary test in this modern clinical environment

because of lack of quantitative analysis and property criteria, resulting

from a strong dependence on the surgeon’s skill and experience.14

Thus, it is possible to improve the detection rate of DRE through the

use of computerized palpation and quantitative analysis to provide

greater accuracy and safer diagnoses for patients.

Physicians usually depend on tactile sensation to guide manipulation

and exploration during open surgery.15 These tactile sensations can

provide the localization and assessment of tumors during open surgery.

Robot assisted minimally invasive surgery motivated by the success of

commercial surgical robots provides advantages over traditional open

surgery, including increased dexterity, precision, and control, but

eliminates the physicians’ tactile sensation.16 Because of the importance

of tactile sensation in localization and surgery, investigations have been

initiated for the use of computerized palpation devices in urology and

other related fields17-25 Computerized palpation is one of major interest

DOI: 10.1007/s12541-013-0321-6

170 / JANUARY 2014 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 1

for medical technique that it could replace subjective palpation and

tactile sensation by acquisition of quantitative tactile feedback. The

palpation results and mechanical property characterization are a possible

solution that facilitates to obtain objective and quantitative information

for abnormal tissue localization during diagnosis and surgery.17-19 A

few researchers have developed computerized palpation systems to

discriminate the target tissues (hard tissue or tumor) from the normal

tissue.20-30 Among the several mechanical palpation techniques, early

indentation have been successfully applied to measure in-vitro tissue

behavior but do not provide a sufficient functionality for in-vivo

localization due to their limited stimulation patterns. Much time and

efforts on the user are required to obtain tissue information for a large

coverage area.6 In clinical palpation, the physicians provide more

complex patterns of finger motion on the tissue surface and combine

motions orthogonal to the surface of the rectum wall with motions

parallel to the surface. Such motions could be more effective by

enhancing the percepts of form,31,32 texture33 and location of tumors.

However, the experimental conditions in the human palpation are very

different from those in one dimensional indentation experiments.

Therefore, the indentation has limited applicability for abnormal tissue

localization. Moreover, for clinical applications, the palpation should

be coupled with image guidance techniques because the palpation tool

cannot reach the target organ without image guidance. Thus, we suggest

a novel sweeping palpation system that can be easily attached to ultra-

sound imaging systems. The sweeping palpation is applied in the

normal direction with a slanted probe followed by rotational motion at

a fixed point. This palpation method is similar to the finger motion

used during palpation by physicians in clinical practice.34 By using

ultrasound imaging system, physicians can obtain the suspected tumor

lesion. Then, our system can be utilized to more precisely focus on the

suspicious tissue area instantly for the accurate needle biopsy.

The property characterization methods suggested by the previous

studies were built on overly simplified assumptions, which limit the

validity of mechanical property characterization. Previous models did

not consider boundary conditions and surface geometry in local

mechanical stimulation experiments and do not include the local

property variance of prostate glands due to the multi-focal nodules of

prostate tumors. It is therefore necessary to develop local prostate

models that cover all of the regions of the prostate. Moreover, prostate

size and geometry vary by patient. For accurate property estimation, we

estimate the mechanical properties of the tissues based on local and

patient-specific biomechanical models while considering surface

geometries using three-dimensional surface reconstructions from

sequential scanning images. We have suggested an localization

procedure to determine the suspicious regions for each subject using

the experimental results. Finally, we have confirmed that the developed

system can distinguish cancerous tissues from normal tissues by

comparing the suspicious regions obtained from the proposed procedure

to pathological tumor maps.

2. Materials and Methods

2.1 Experimental setup

Through discussions with physicians on the required range and

space constraints of DRE motion, a set of preliminary requirements for

the palpation module was prepared. The probe tip should pass through

the small orifice (anus) when entering the body. The motion range of

the palpation module should be larger than 100 mm straight from the

entry site and should cover 40 mm (width) × 40 mm (height) in place

to reach the target (prostate). The system is designed to induce a

palpation to the target tissue and to measure the tissue behavior against

the palpation. The system dimension and operation range can be

determined using the comments of the physicians. The ultrasound-

guided palpation system consists of a three link system, an ultrasound

probe, and a palpation module. The three link system with two encoders

(E20s, Autonics) was developed to support the ultrasound probe and

track the probe motion by measuring the angle of yaw and pitch

direction. The ultrasound probe system (8808, BK Medical, USA) was

used to align the palpation module to the target regions of tissues. The

palpation module is composed of a C-500 sensor (Tactarray, USA) and

the attached frame with the ultrasound probe. The palpation module is

located on the opposite side of the ultrasound probe module. The

human prostate is suspended by the pubo-urethral ligaments and pubo-

prostatic ligaments. The dorsal midline fibrous raphe dorsally anchors

the prostate with Denonvilliers’ fascia, and the prostate is supported by

the pelvic bones.35 In addition, a layer of fat covers the prostate. These

anatomical features and in vivo experimental conditions should be

Fig. 1 (a) Ultrasound probe, palpation module, and palpation module

support; (b) Combination of the DRE simulator model and an artificial

support

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 1 JANUARY 2014 / 171

taken into consideration to obtain more precise properties of the

prostate. Thus, for the simulated environmental condition, an artificial

support in the DRE simulator model (MK-2, Limbs and things, USA)

was developed by considering the pelvic bone and the ligaments as

shown in Fig. 1. The three dimensional morphology of the bone and

ligament around the prostate tissue was segmented by urologic experts.

Based on the obtained morphology, a three dimensional printer (rapid

prototype) was used to develop the artificial support.

2.2 Sweeping palpation experiments

All patients provided written informed consent, and the study was

approved by the Severance Hospital Institutional Review Board (IRB

No: 1-2011-0048). Prostate specimens were obtained from 10 patients

who had undergone radical prostatectomy at Severance Hospital, Yonsei

University, Seoul, Korea from October 2011 to March 2012. The mean

age of patients was 60.6±5.71 years (range 51-70 years), and the mean

value of PSA level was 15.1±13.5 ng/mL (range, 4.9-37.7 ng/mL). The

mean preoperative volume of the prostates was 26.4±7.7 mL (range,

18.3-39.4 mL). The pathologic cancer stage was T2a in 1 patient, T2c

in 3 patients, T3a in 4 patients, and T3b in 2 patients. The investigator

who conducted the experiments was blind to the clinical information.

The specimen is placed and fixed on the artificial support in the DRE

simulator. The US probe system is brought into contact with the

specimen through the DRE simulator model’s anus to align the target

regions and the US probe. When the US probe is rotated 180 degrees

along the x-axis (red line), the force sensor tip induces a 3 mm deep

sweeping palpation as shown in Fig. 2. The sweeping palpation consists

of a normal directional motion with a slanted probe and a rotational

motion at a fixed point. Using the sweeping palpation, the system can

access the rectal space, via the anus, which has a smaller diameter.34

The reaction forces were obtained with a data acquisition system. The

task sequence as outlined above is repeated at twelve regions to

identify suspect areas. The experiments were performed within 30

minutes after the extraction of the specimen in the operating room. For

each prostate specimen, the sweeping palpation experiments were

performed at 12 regions across the posterior surface as shown in Fig.

3(c). These 12 regions were referenced by those of double-sextant

needle biopsies, including the lateral apex (LA), lateral-mid (LM),

lateral base (LB), medial apex (MA), medial mid (MM), and medial

base (MB). A total of 120 indentations were performed on the 10

specimens.

2.3 Mechanical property characterization and localization

For accurate property characterization, laser scanning systems

(Pythagoras, Cylod, USA) were used to obtain the three dimensional

morphologies of the resected prostate tissues, as shown in Fig. 3. The

morphologies were converted to geometric finite element (FE) models

of the prostate tissues. The simulated model consisted of 112,710 nodes

with a total of 105,000 brick elements (C3D8). A no-slip condition was

assumed between the tissue and the artificial support. The normal and

tangential contact between the probe tip and the tissue was assumed to

be hard and frictionless. The elastic modulus and Poisson’s ratio were

used to represent mechanical properties of the prostate. The prostate

was assumed to be incompressible (v = 0.499). The FE simulations

were performed using the ABAQUS (SIMULIA, USA).36 The reaction

force values of the simulation were fitted to those of the experimental

results. The estimated elastic moduli were compared with the local

normal criteria to localize the prostate tumor. The steps for localizing

the tumor with the local normal criteria are shown in Fig. 4.37 Local

reaction forces are measured from the twelve region sweeping

experiments, and then local elastic moduli are estimated by the local

prostate model (Fig. 4(b)). The local normal criteria represent the sum

of the mean value and standard deviation (Fig. 4(a) and Table 1). The

Fig. 2 (a) Ultrasound probe, palpation module, and palpation module

support; (b) Combination of the DRE simulator model and an artificial

support

Fig. 3 (a) 3D geometric analysis of resected prostate tissue and 3D

reconstruction for FE analysis, (b) local FE models and (c) double

sextant sections


localization step was performed by comparing the estimated local elastic

moduli and the normal property criteria (Fig. 4(c)). Then, we compared

the obtained localization results to the pathological results, which had

been confirmed by an uro-pathologist who was blinded to the conditions

of the study.

3. Results

The overall mechanical properties of 120 regions from 10 prostate

specimens were obtained using the experimental results and the FEM-

based mechanical property characterizations. Since the geometries and

contact conditions of the prostate tissue are not flat and smooth surface,

the force responses were not identical with ideal results (FE simulation

results). In Fig. 5(a), the normal property criteria were used for

comparison with the properties of the ten subjects. Fig. 5(a) represents

the optimized elastic modulus of the ten subjects by regions. Using the

Fig. 4 Steps to localize and differentiate abnormalities using the optimized elastic modulus database and case study results (a) Local normal tissue

criteria from the CCP model database (b) Local property estimation from the experimental results and homogenous model characterization (c)

Localization using the comparison of experimental results and local normal criteria

Table 1 The UE and the FRE for each subject (mean standard deviation). The UE was measured during 10 trials on each subject in the free metallic

environment

LB MB LM MM LA MA

Normal tissue 17.28±6.60 18.6±7.25 13.05±6.25 13.88±5.80 13.24±6.25 12.07±5.90

n 32 43 32 39 28 32

Fig. 5 (a) Local normal property criteria and local mechanical properties

of all subjects (b) Typical stress distributions for local property

estimation

Table 2 The UE and the FRE for each subject (mean standard deviation).

The UE was measured during 10 trials on each subject in the free

metallic environment

Subject number Mechanical analysis results Pathological results

A 4,8,12 4,8,11,12

B 1,2,3,4 1,2,3,4,6,7

C 1,2,3,4,7 1,2,3,4,6,7

D 1,2,4,5,6,7,8,11,12 1,2,3,4,5,6,7,8,11,12

E 1,2,3,4,5,6,7,8,9,12 1,2,3,4,5,6,7,8

F 1,2,3,4,6,7,8,12 1,2,3,4,6,7,8

G 1,4,5,8,9,10,11,12 1,4,5,8,12

H 1,2,3,4,5,9,10,11 1,2,3,4,5

I 1,2,3,4,5,6,7,8,10,11,12 1,2,3,4,5,6,7,10

J 1,2,4,5,7,8,9,11,12 1,2,3,4,7,11


localization steps, we can determine the suspicious regions for each

subject and compare them to the pathological results, as shown in Table

2. The mechanical localization was performed using statistical

significance (p-values < 0.01) between the normal property criteria and

the optimized properties of the ten subjects for each region. Table 2

shows the mechanical localization results and the pathological results

that had been confirmed by an uro-pathologist under blinded conditions.

The concordance rate between the localization results and the pathologic

results was 81.7 % (98/120). The sensitivity and specificity of the

proposed diagnostic system were 90.7% (59/65) and 70.9% (39/55),

respectively. While the positive predictive value (PPV) 78.6% (59/75),

negative predictive value (NPV) was 86.7% (39/45).

4. Discussion

In this paper, we proposed the normal property criteria and a

localization procedure to determine the suspicious regions for each

subject by using the local FE prostate model fitting with experimental

results. Then, we compared the suspicious regions from mechanical

localization with the pathological results. From this comparison, results

showed high sensitivity, specificity, NPVs, and PPVs. The developed

system could distinguish tumor regions more effectively compared to

the conventional DRE because of the following reasons. While the

layers of rectal wall, and Levator fascia, prostatic fascia, Denovilier’s

fascia and fat covers the prostate in in-vivo condition, these anatomical

features are not included in our simulated environment conditions. Our

palpation system has direct contact with resected prostate tissue while

the physician’s fingertip in DRE was interfered by the anatomical

features above the prostate. Gwilliam et al.16 and Salvazyan et al.38

reported the findings that computerized palpation is more effective at

detecting tumor than human finger and our results also supports the

findings. The developed system and proposed diagnostic procedure can

be used as a good screening and confirmation protocol for prostate

tumor. Physicians can obtain mechanical information on the target

tissues that can be used to designate the suspicious tissue regions of

patients more precisely than DRE.

Important issues are the experimental conditions related to prostate

geometry and the size of the probing tips. If the sample thickness is

comparable to the size of the tip, the hardness of the underlying substrate

may influence the apparent mechanical properties.39 In addition, prostate

size and geometry vary by patient. For a more accurate characterization,

the morphologies of the resected prostate tissue should be obtained for

modeling of the prostate. In this study, we used the laser scanner for

geometric scanning and reconstructed the prostate model for patient-

specific property characterization. As a result, our characterization

results showed high sensitivity, specificity, NPV, and PPV. Despite these

improvements, there still remain limitations; the palpation at apex regions

shows lower PPV compared to that of the mid and base because

thickness of apex regions is relatively thinner than that of other regions.

While tissue properties were obtained in both the peripheral zones with

good correlation to pathological results, the deepest transition zone may

not be correctly assessed by using our system. Gwillium et al. shows

that large tumor closer to the surface are easiest to detect while small

tumor embedded deepest are most difficult to detect.16 Bloom et al.

shows that detectability of tumor are most related with tumor depth

from the exposed surface of the tissue among the stimulus parameters

including tumor size, depth, hardness, and mobility.40 In addition, the

prostate hyperplasia, prostatic calcification, cysts and inflammatory

granuloma might have induced changes in the elasticity of the prostate

in this study.41

The choice of diagnostic criteria is also dependent on how the data

in both elastography and DRE are used.42 One of the most challenging

issues in mechanical diagnosis is the choice of diagnostic criteria. Every

physician has different and subjective standards in regard to DRE

findings; these differences lead to miscommunication in the exchange

of information about disease entities among physicians. In this study,

we proposed normal property criteria (sum of the mean and standard

deviation) that determine the suspicious regions for each subject and

compared them to the pathological results of the cancer location. There

are additional values for diagnostic criteria, such as the maximum of

normal properties and the highest quartile of normal properties. To

compare the results, statistical analysis was carried out to obtain a PPV

for each criterion. The PPVs of the criteria were 85.1% (40/47) and

68.5% (61/89). The sensitivities of the criteria were 61.5% (40/65) and

93.8% (61/65). We used the sum of the mean values and standard

deviations as the diagnostic standard in this study.

5. Conclusion

In this paper, we developed a palpation system that can be used to

overcome the limitations of the conventional DRE method. To

demonstrate its safety and potential application as a clinically practical

tool for prostate tumor detection, our systems could be easily attached

to an ultrasound probe or elastography system. Through ultrasound

imaging, physicians can visualize the suspected lesion. Then, our system

can be utilized to instantly and more precisely focus on the suspected

tissue area. We proposed criteria and a localization procedure to

determine the suspicious regions for each subject using the FE prostate

model fitting with experimental results. Then, we compared the suspicious

regions from mechanical analysis with the pathological results.

REFERENCES

1. Ramon, J. and Denis, L. J., “Prostate Cancer: Recent Results in

Cancer Research,” Springer, 2007.

2. Byar, D. P. and Mostofi, F. K., “Carcinoma of the Prostate: Prognostic

Evaluation of Certain Pathologic Features in 208 Radical

Prostatectomies,” Cancer, Vol. 30, No. 1, pp. 5-13, 1972.

3. Catalona, W. J., Smith, D. S., Ratliff, T. L., Dodds, K. M., Coplen,

D. E., and et al., “Measurement of Prostate-Specific Antigen in

Serum as a Screening Test for Prostate Cancer,” New England

Journal of Medicine, Vol. 324, No. 17, pp. 1156-1161, 1991.

4. Brawer, M. K., Chetner, M. P., Beatie, D. M., Vessella, R. L., and

Lange, P. H., “Screening for Prostatic Carcinoma with Prostate

Specific Antigen,” Journal of Urology, Vol. 147, No. 3, pp. 841-845,

1992.


5. Partin, A. W., Catalona, W. J., Southwick, P. C., Subong, E. N.,

Gasior, G. H., and Chan, D. W., “Analysis of Percent Free Prostate-

Specific Antigen (PSA) for Prostate Cancer Detection: Influence of

Total PSA, Prostate Volume, and Age,” Urology, Vol. 48, No. 6, pp.

55-61, 1996.

6. Silletti, J. P., Gordon, G. J., Bueno, R., Jakitsch, M., and Loughlin,

K. R., “Prostate Biopsy: Past, Present, and Future,” Urology, Vol. 69,

No. 3, pp. 413-416, 2007.

7. Mettlin, C., Lee, F., Drago, J., and Murphy, G. P., “The American

Cancer Society National Prostate Cancer Detection Project. Findings

on the Detection of Early Prostate Cancer in 2425 Men,” Cancer,

Vol. 67, No. 12, pp. 2949-2958, 1991.

8. Bozeman, C. B., Carver, B. S., Caldito, G., Venable, D. D., and

Eastham, J. A., “Prostate Cancer in Patients with an Abnormal

Digital Rectal Examination and Serum Prostate-Specific Antigen

Less than 4.0 ng/mL,” Urology, Vol. 66, No. 4, pp. 803-807, 2005.

9. Pallwein, L., Mitterberger, M., Struve, P., Horninger, W., Aigner, F.,

and et al., “Comparison of Sonoelastography Guided Biopsy with

Systematic Biopsy: Impact on Prostate Cancer Detection,” European

Radiology, Vol. 17, No. 9, pp. 2278-2285, 2007.

10. Fujita, K., Landis, P., Mcneil, B. K., and Pavlovich, C. P., “Serial

Prostate Biopsies are Associated with an Increased Risk of Erectile

Dysfunction in Men with Prostate Cancer on Active Surveillance,”

The Journal of Urology, Vol. 182, No. 6, pp. 2664-2669, 2009.

11. Schröder, F. H., Kruger, A. B., Rietbergen, J., Kranse, R., van der

Maas, P., and et al., “Evaluation of the Digital Rectal Examination

as a Screening Test for Prostate Cancer,” Journal of the National

Cancer Institute, Vol. 90, No. 23, pp. 1817-1823, 1998.

12. Gosselaar, C., Roobol, M. J., Roemeling, S., van der Kwast, T. H.,

and Schröder, F. H., “Screening for Prostate Cancer at Low PSA

Range: the Impact of Digital Rectal Examination on Tumor Incidence

and Tumor Characteristics,” The Prostate, Vol. 67, No. 2, pp. 154-161,

2007.

13. Catalona, W., Richie, J., Ahmann, F., Hudson, M., Scardino, P., and

et al., “Comparison of Digital Rectal Examination and Serum Prostate

Specific Antigen in the Early Detection of Prostate Cancer: Results

of a Multicenter Clinical Trial of 6,630 Men,” The Journal of

Urology, Vol. 151, No. 5, pp. 1283-1290, 1994.

14. Smith, D. S., Catalona, W. J., and Herschman, J. D., “Longitudinal

Screening for Prostate Cancer with Prostate-Specific Antigen,”

JAMA: the Journal of the American Medical Association, Vol. 276,

No. 16, pp. 1309-1315, 1996.

15. Lederman, S. J. and Klatzky, R. L., “Sensing and Displaying Spatially

Distributed Fingertip Forces in Haptic Interfaces for Teleoperator

and Virtual Environment Systems,” Presence: Teleoperators and

Virtual Environments, Vol. 8, No. 1, pp. 86-103, 1999.

16. Gwilliam, J. C., Pezzementi, Z., Jantho, E., Okamura, A. M., and

Hsiao, S., “Human vs. Robotic Tactile Sensing: Detecting Lumps in

Soft Tissue,” Proc. of IEEE Haptics Symposium, pp. 21-28, 2010.

17. Smith, D. S. and Catalona, W. J., “Interexaminer Variability of

Digital Rectal Examination in Detecting Prostate Cancer,” Urology,

Vol. 45, No. 1, pp. 70-74, 1995.

18. Dario, P. and Bergamasco, M., “An Advanced Robot System for

Automated Diagnostic Tasks Through Palpation,” Proc. of IEEE

Transaction on Biomedical Engineering, Vol. 35, No. 2, pp. 118-126,

1988.

19. Bicchi, A., Canepa, G., De Rossi, D., Iacconi, P., and Scillingo, E. P.,

“A Sensor-Based Minimally Invasive Surgery Tool for Detecting

Tissue Elastic Properties,” Proc. of IEEE International Conference

on Robotics and Automation, Vol. 1, pp. 884-888, 1996.

20. Scilingo, E., Rossi, D., Bicchi, A., and Iaccone, P., “Haptic Display

for Replication of Rheological Behavior of Surgical Tissues:

Modelling, Control and Experiments,” Proc. of 6th Haptic Interfaces

for Virtual Environment and Teleoperator Systems Symposium, pp.

173-176, 1997.

21. Yan, J., Scott, P. K., and Fearing, R. S., “Inclusion Probing: Signal

Detection and Haptic Playback of 2D FEM and Experimental Data,”

Proc. of IMECE, Vol. 67, pp. 203-210, 1999.

22. Chonan, S., Jiang, Z. W., Tanaka, M., Kato, T., Kamei, M., and

Tanahashi, Y., “Development of a Palpation Sensor for Detection of

Prostate Cancer and Hypertrophy (Optimum Structural Design of

Sensor),” International Journal of Applied Electromagnetics and

Mechanics, Vol. 9, No. 1, pp. 25-38, 1998.

23. Tanaka, M., Kamei, M., Furubayashi, M., Jiang, Z., Tanahashi, K.,

and Chonan, S., “Development of a Palpation Sensor for Detecting

Prostate Cancer and Hypertrophy (Signal Processing on Clinical Data),”

Transactions on the Japan Society of Mechanical Engineers, Vol. 65,

pp. 3296-3301, 1999.

24. Tanaka, M., Furubayashi, M., Tanahashi, Y., and Chonan, S.,

“Development of an Active Palpation Sensor for Detection Prostatic

Cancer and Hypertrophy,” Smart Materials and Structures, Vol. 9,

No. 6, pp. 878-884, 2000.

25. Liu, H., Noonan, D. P., Althoefer, K., and Seneviratne, L. D., “Rolling

Mechanical Imaging: A Novel Approach for Soft Tissue Modelling

and Identification during Minimally Invasive Surgery,” Proc. of IEEE

International Conference on Robotics and Automation, pp. 845-850,

2008.

26. Liu, H., Noonan, D. P., Challacombe, B. J., Dasgupta, P., Seneviratne,

L. D., and Althoefer, K., “Rolling Mechanical Imaging for Tissue

Abnormality Localization During Minimally Invasive Surgery,” Proc.

of IEEE Transactions on Biomedical Engineering, Vol. 57, No. 2, pp.

404-414, 2010.

27. Sangpradit, K., Liu, H., Seneviratne, L.D., and Althoefer, K., “Tissue

Identification using Inverse Finite Element Analysis of Rolling

Indentation,” Proc. of IEEE international Conference on Robotics

and Automation, pp. 1250-1255, 2009.

28. Sangpradit, K., Liu, H., Dasgupta, P., Althoefer, K., and Seneviratne,

L. D., “Finite-Element Modeling of Soft Tissue Rolling Indentation,”


Proc. of IEEE Transactions on Biomedical Engineering, Vol. 58, No.

12, pp. 3319-3327, 2011.

29. Ahn, M., Kim, J., Ian, L., Rha, K. H., and Kim, H. J., “Mechanical

Property Characterization of Prostate Cancer using a Minimally

Motorized Indenter in an Ex vivo Indentation Experiment,” Urology,

Vol. 76, No. 4, pp. 1007-1011, 2010.

30. Ahn, B., Lorenzo, E. I., Rha, K. H., Kim, H. J., and Kim, J., “Robotic

Palpation-Based Mechanical Property Mapping for Diagnosis of

Prostate Cancer,” Journal of Endourology, Vol. 25, No. 5, pp. 851-

857, 2011.

31. Goodwin, A. W., John, K. T., and Marceglia, A. H., “Tactile

Discrimination of Curvature by Humans using only Cutaneous

Information from the Fingerpads,” Experimental Brain Research,

Vol. 86, No. 3, pp. 663-672, 1991.

32. Goodwin, A., Macefield, V., and Bisley, J., “Encoding of Object

Curvature by Tactile Afferents from Human Fingers,” Journal of

Neuro-physiology, Vol. 78, No. 6, pp. 2881-2888, 1997.

33. Srinivasan, M., Lamotte, R, “Tactual Discrimination of Softness,”

Journal of Neuro-physiology, Vol. 73, No. 1, pp. 88-101, 1995.

34. Ahn, B., Kim, Y., Oh, C., and Kim, J., “Robotic Palpation and

Mechanical Property Characterization for Abnormal Tissue

Localization,” Medical & Biological Engineering & Computing,

Vol. 50, No. 9, pp. 961-971, 2012.

35. Schwenninger, D., Schumann, S., and Guttmann, J., “In vivo

Characterization of Mechanical Tissue Properties of Internal Organs

using Endoscopic Microscopy and Inverse Finite Element Analysis,”

Journal of biomechanics, Vol. 44, No. 3, pp. 487-493, 2011.

36. Choi, H. W. and Kim, Y. E., “Contribution of Paraspinal Muscle and

Passive Elements of the Spine to the Mechanical Stability of the

Lumbar Spine,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 6, pp. 993-

1002, 2012.

37. Kim, Y., Ahn, B., Lee, J. W., Rha, K. H., and Kim, J., “Local Property

Characterization of Prostate Glands using Inhomogeneous Modeling

Based on Tumor Volume and Location Analysis,” Medical &

Biological engineering & Computing, Vol. 51, No. 1-2, pp. 197-205,

2013.

38. Salvazyan, A., “Computerized Palpation is More Sensitive than

Human Finger,” Proc. of the 12th International Symposium on

Biomedical Measurements and Instrumentation, pp. 523-524, 1998.

39. Radmacher, M., “Measuring the Elastic Properties of Biological

Samples with the AFM,” Proc. of IEEE Engineering in Medicine

and Biology Magazine, Vol. 16, No. 2, pp. 47-57, 1997.

40. Bloom, H. S., Criswell, E. L., Pennypacker, H. S., Catania, A. C., and

Adams, C. K., “Major Stimulus Dimensions Determining Detection

of Simulated Breast Lesions,” Perception & Psychophysics, Vol. 32,

No. 3, pp. 251-260, 1982.

41. Galosi, A. B., Montironi, R., Fabiani, A., Lacetera, V., Gallé, G., and

Muzzonigro, G., “Cystic Lesions of the Prostate Gland: An Ultrasound

Classification with Pathological Correlation,” The Journal of Urology,

Vol. 181, No. 2, pp. 647-657, 2009.

42. Evans, A., Whelehan, P., Thomson, K., McLean, D., Brauer, K., and

et al., “Quantitative Shear Wave Ultrasound Elastography: Initial

Experience in Solid Breast Masses,” Breast Cancer Research, Vol.

12, No. 6, pp. 1-11, 2010.

Documents

Digital rectal examination in a simulated environment using sweeping palpation and mechanical localization