Telehealth and Computeraided Diagnosis
By Juan Shan
April 2013
Telehealth
 Telehealth is the delivery of
health-related services and
information via
telecommunications
technologies.
 Telehealth could be as simple
as two health professionals
discussing a case over the
telephone or as complicated
as doing robotic surgery
between facilities at different
ends of the globe.
Telehealth and Computer-aided
Diagnosis
 Telehealth is an interdiscipline that involes medical
image processing, networking, cybersecurity, machine
learning, database management, and data mining.
 Computer-aided diagnosis (CAD) systems use computer
techniques to assist doctors to analyze data and make
diagnosis.
 Computer-aided diagnosis (CAD) systems could be able
to serve independently as one terminal in the telehealth
network in the future.
CAD for Breast Cancer
• Breast cancer is the #1 leading
cause of cancer death for women
at ages 20 to 59 [1,2].
• 226,870 newly diagnosed cases
and 39,510 deaths in the United
States in 2012 [2].
• The earlier the cancers are
detected, the better the patients
are cured [1].
Fig. 1. Incidence rate of cancers
for females
Mammography
 Previously, the most effective modality for detecting breast
cancer is mammography.
 Limitations of mammography:
1. The radiation might be harmful for
the patients and radiologists.
2. High false positive rate: 65%–85%.
3. Hardly detect breast cancer for
dense breasts.
Fig. 2. Mammography images
Ultrasound Imaging
 Breast ultrasound (BUS) imaging is superior to the
mammography:
1. Having no radiation, safer than mammography for patients and
radiologists in daily clinical practice [14].
2. Cost effective and portable.
3. More sensitive than mammography for dense breasts ( i.e.,
suitable for women younger than 35 years old[12]).
Computer-aided Diagnosis (CAD)
Advantages of a CAD system:
 Calculate computational features
and statistical features, which
cannot be obtained visually or
intuitively by humans (doctors).
 Minimize the operatordependent nature of ultrasound
imaging [4] and make the
diagnosis process reproducible.
Computer-aided Diagnosis (CAD)
1. Preprocessing: enhance the
image contrast and reduce
speckle noise.
2. Segmentation: separate the
lesion from the surrounding
tissues.
3. Feature extraction: extract
critical features to distinguish
benign and malignant lesions.
4. Classification: use machine
learning techniques to classify
the lesion into benign or
malignant types.
Computer-aided Diagnosis (CAD)
 Segmentation is the most
important step.
 Since many crucial features to
distinguish benign and malignant
lesions are based on the shape
and boundary of the lesion.
Example of benign and malignant lesions
Benign
Malignant
 Lesion segmentation is important and…
difficult as well!
 Due to the nature of ultrasound imaging, the breast
ultrasound (BUS) images are degraded by speckle noise,
low contrast, blurred edges and shadow effect.
 Automatic segmentation is a challenging task.
An automatic segmentation method
J. Shan, H. D. Cheng and Y. X. Wang,
“Effective and Automatic Breast
Ultrasound Image Segmentation Using
L-Means Clustering”, Medical Physics.
Vol. 39, Issue 9, pp. 5669-5682,
2012 Sep
The proposed method
 ROI generation
 Speckle reduction
 Contrast
enhancement
 Clustering
ROI Generation
Binarize the image into background and foreground,
by an adaptively selected threshold
(a) Original image
(b) Binarized Image
ROI Generation
Delete the boundary-connected regions and noise
regions.
(c) Image after binarization
(d) Image after region deletion
ROI Generation
Rank the regions.
Sn 
A
, n  1,..., k
dis (Cn , C0 )  var(Cn )
The one with the highest
score is considered as the
lesion region.
(e) Image of the winning region
ROI generation result
(a) Original BUS image
(b) Binary image
(d) ROI generation
(c) Winning region
The proposed method cont.
 ROI generation
 Speckle reduction
[5]
 Contrast enhancement
 Neutrosophic l-means
Speckle reduction
An effective and fast algorithm is used:
speckle reducing anisotropic diffusion (SRAD)
[5]
The proposed method cont.
 ROI generation
 Speckle reduction
[5]
 Contrast enhancement
 Neutrosophic l-means
Fourier Transform
 Spatial domain  Frequency domain
 1-dimentional
From 1-D signal to 2-D image
 2-D Log-Gabor filters defined in polar coordinates:
(log(w / w0 ))2 (q  q 0 )2
G (w ,q )  exp (

)
2
2
2(log(k / w0 ))
2s q
where k is related to the bandwidth of the filter and w0 is the
center frequency of the filter. q0 is the orientation. sq defines
the spread of the Gaussian orientation function.
 6 orientations (0°, 30°, 60°, 90°, 120°, 150°) are chosen to
cover the whole spectrum.
 In each orientation, local phase feature LPA is calculated.
PMO Image
 Which orientation should be
used?
 The LPA in the direction of the
edge can better characterize the
structure than the LPAs in other
directions.
 Phase in max-energy orientation
= PMO
n
n
Engq  ( e(s)) ( o( s)) 2
2
s 1
s 1
How to process in frequency domain
to get PMO image
(log(w / w0 ))2 (q  q 0 )2
G (w ,q )  exp (

)
2(log(k / w0 ))2
2s q2
phase( x, s) || angle( I '( x, s)) |||| tan 1 (imag( x, s) / real( x, s)) ||
phase '  [π  π *cos(2* phase)] / 2
LPAq 
1 n
 phase '(s)
n s 1
n
n
s 1
s 1
Engq  ( e(s))2 ( o( s)) 2
PMO(i, j )  LPA (i, j ),   arg max
Eng (i, j)
q
q 0, 30, 60, 90, 120, 150
1  4( PMO  0.5)2 0  PMO  0.5
PMO  
1
0.5<PMO  1

2 PMO2
PMO  
2
1  2(1  PMO)
0  PMO  0.5
0.5<PMO  1
PMO image example
(a)
(b)
(a) ROI
(b) De-speckled image
(c) PMO image
(d) Enhanced PMO image
(c)
(d)
The proposed method
 ROI generation
 Speckle reduction
[5]
 Contrast enhancement
 Clustering
Clustering
 Clustering is the partitioning of a data set into subsets
so that the data in each cluster have some common
attributes.
 Basic clustering:
 http://home.deib.polimi.it/matteucc/Clustering/tutori
al_html/AppletKM.html
The proposed method
 ROI generation
 Speckle reduction
[5]
 Contrast enhancement
 Clustering
Evaluation
Breast ultrasound database
 The database is composed of 120 BUS images. 58 cases
are benign, 62 cases are malignant.
 Every lesion is manually outlined by an experienced
radiologist. The manual delineations are served as the
standard to evaluate the segmentation method.
Comparison is necessary
 Compare the new method with:
 A segmentation method using active contour model
 A segmentation method using level-set model [28]
 A segmentation method using watershed model [70]
[21]
Result of segmentation methods
(a) The original image. (b) Manual delineation by radiologist. (c) Output of the
method in [21]. (d) Output of the method in [28]. (e) Output of the method in [70].
(f) Output of the proposed method.
Area error metrics
Accuracy comparison
Future direction
 Continue the research on breast cancer ultrasound, to
find more reliable segmentation methods, to extract
the features of tumors, and to train classifiers that can
automatically classify tumors into benign/malignant.
 Dental X-rays dataset, to detect tooth root and dental
diseases.
 Other type of medical images and explore the possible
application of computer-aided diagnosis.
Thanks!
Questions?
References
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