Curve based Fast Detail Enhancement for Biomedical Images

Ran Fei

1,2

, Ying Weng

1,2,*

, Yiming Zhang

1,2

and Jonathan Lund

1,2

School of Computer Science, University of Nottingham Ningbo China, China

School of Medicine, University of Nottingham, U.K.

Keywords: Image Enhancement, Contrast Enhancement, Histogram Equalization, Biomedical Imaging.

Abstract: Biomedical images are widely collected from various applications, which are used for patients' screening,

diagnosis and treatment. The dark regions of biomedical images may play as an important role as the bright

regions. The enhanced details in the dark regions of biomedical images simultaneously maintain the quality

of the rest of the images and reveal more information for doctors and surgeons in medical procedures. This

paper proposes a fast method to adaptively enhance the details in the dark regions of biomedical images,

including X-rays, video frames of laparoscopy in minimally invasive surgery (MIS).

1 INTRODUCTION

Biomedical image processing is a broad and complex

field. In order to diagnose and treat patients,

biomedical images are essential. Low quality and

contrast of biomedical images will reduce the doctor's

ability to analyze the images, causing subsequent

processing difficulties. Moreover, due to medical

devices and procedures, doctors may have limited

controls in acquiring medical images leaving

biomedical images with non-homogeneity of

luminance and contrast levels. Hence it is vital to

enhance the biomedical image quality as well as

improve the image contrast. For instance, X-rays may

be low in contrast and details. Frames obtained during

minimally invasive surgery may have a large shaded

region due to less adequate light introduced into the

cavity; dark-colored tissue may lack details in high

contrast frames. Then it is essential to recognize

images that need enhancement then adaptively select

the targeted dark regions for further processing and

image contrast enhancement.

Many image enhancement techniques have been

developed by researchers including fuzzy set theory

image enhancement method (Preethi & Rajeswari,

2013), histogram equalization image enhancement

method (Agaian et al., 2007), histogram matching

image enhancement method (Irmak & Ertas, 2016)

and equalized histogram equalization image

enhancement method (Kadhum, 2012). Besides, there

Corresponding author.

are other image enhancement techniques, such as

nonlinear image enhancement technique (Singh et al.,

2015; Yaping et al., 2012) and wavelet transform

technique (Singh et al., 2015; Premkumar et al., 2014;

Ehsani et al. 2011).

Also, in image enhancement, one of the most

generally used and essential technique is contrast

enhancement. The contrast enhancement's primary

purpose is to adjust the local contrast to bring out the

image's exact regions. Contrast stretching is a contrast

enhancement technique used to extend an image's

dynamic range (Zakaria et al., 2010). Other image

contrast enhancement methods, including

homomorphic filtering (Zakaria et al., 2010), retinex

(Chen & Beghdadi, 2010), and histogram

equalization (Kim, 1997). Our proposed method is

based on histogram equalization. The proposed

method consists of two parts: the first part divides

images into different intensity regions; the second

part will further process the dark regions of the

targeted images.

The rest of this paper is organized as follows.

Section 2 deals with the previous work of the

histogram equalization (HE) methods. Section 3

presents our proposed curve based fast detail

enhancement method. The experiment results are

shown in section 4, and section 5 gives the conclusion

of the paper.

Fei, R., Weng, Y., Zhang, Y. and Lund, J.

Curve based Fast Detail Enhancement for Biomedical Images.

DOI: 10.5220/0010250203370344

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP, pages

337-344

ISBN: 978-989-758-488-6

337

2 PREVIOUS WORK

Histogram equalization (HE) based algorithms,

adjusting gray-level distributions are commonly

deployed in contrast enhancement as it is simple to

use on biomedical images. It increases the dynamic

range expansion by increasing each pixel's value,

thereby enhancing the input image's contrast and

brightness (Kim, 1997). This section summarizes the

work on histogram equalization (HE) based

algorithms previously proposed by various

researchers.

Global histogram equalization stretches the image

intensity level to the whole range of 8-bit values, 0 –

255, effectively increasing the dark-bright contrast of

images (Mokhtar et al., 2009). Global HE is

discriminative. It may introduce undesired intensity

changes in a large block and may increase the noise

level of images. Local HE was proposed to get rid of

specific Global HE issues, but the method a) requires

high computational cost, b) the output appearance

depends on the size of selected local areas (Abdullah-

Al-Wadud et al., 2007). Similarly, adaptive HE

algorithms, taking advantage of global and local HE,

were introduced to enhance broader applications

(Singh et al., 2016).

Another research brightness preserving bi-

histogram equalization (BBHE) is conducted by Kim

(1997), which can preserve brightness and avoid false

coloring. Moreover, dualistic sub-image histogram

equalization (DSIHE) is similar to BBHE that input

histogram is decomposed into two subsections (Patel

et al., 2013). DSIHE performs better than BBHE

regarding entropy and brightness preservation

(Kalhor et al., 2019). Recursive mean separated

histogram equalization (RMSHE) is an extended

version of BBHE (Patel & Muthu, 2020). Compared

to BBHE, RMSHE can preserve the original

brightness of the image. Besides, dynamic histogram

equalization (DHE) assists in the control of the effect

of an image without losing important information in

the image (Abdullah-Al-Wadud et al., 2007).

Besides, a method named contrast limited

adaptive histogram equalization (CLAHE) is

proposed by Reza (2004) used with Ostu to enhance

biomedical image. A novel contrast enhancement

algorithm Histogram equalization with adaptive

gama correction and homomorphic filtering

(QWAGC-FIL) was developed by Monika Agarwal

et al. (2017). This algorithm can enhance the image

with low contrast while maintaining maximum

entropy and enhancement control.

3 THE PROPOSED METHOD

The method proposed in this paper take advantages of

global histogram equalization (GHE). As shown in

Figure 2, an adaptive intensity mapping is applied

before GHE to compensate issues of noise and

unwanted colour boundaries, Figure 1.

(a) Input (b) After GHE

Figure 1: Issues of global histogram equalization, (a) and

(b), unwanted colour boundaries in dark regions; (c) and

(d), noise around edges are amplified after global histogram

equalization.

The intensity mapping is done in the V channel of

HSV colour space, where V = max (R, G, B). To

calculate the mapping function, the input V channel

should be divided into 3 sub-images, a noise part to

determine the offset of the function, a relevant dark

part to be mapped to a brighter value region and the

bright part to determine how bright the dark part can

be mapped to. To obtain the three parts, Ostu

threshold is applied to the input channel, which output

2 sub-images where the inter-class variance of the

histogram of the two sub-images are maximised.

After the initial thresholding:

1) If the dark part average is smaller than 32

(thresh_low) and the bright part average is

greater than 64, the dark part will be used to

measure the noise level; if the bright part

average is smaller than 64, the image is an

almost black image and is not currently

considered in this paper.

2) If the dark part is recognised as the noise

part, the bright part will be further divided

into 2 sub-images using Ostu method. The

averages and derivatives of the relevant dark

and bright parts (ave

, det

and ave

, det

)

and the maximum of the dark region, max

are recorded as respectively.

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338

Figure 2: Workflow of the proposed algorithm.

Figure 3: Ostu thresholding.

3) If the initial divided dark part has averages

larger than 32, this dark part is further

divided until the noise part with mean value

smaller than 32 is separated, the average of

noise part is noted as ave

4) After that, the rest of the image will be

threshold (Ostu) into bright and dark parts.

The average, variance, maximum, ave

det

, max

and ave

, det

will be calculated

accordingly.

Averages and derivatives of separated dark and

bright parts are used to determine the shape of curve

applying to the input images. Curves are designed

following rules:

a) After applying the curves, pixels in the dark parts

should have smaller values than pixels in the

bright parts;

b) Logarithm curves g





N

N

ln



 is

applied to pixels larger than N

to improve the

perceptive linearity of the relevant dark region

c) Near saturation region is supressed using sinuous

function to reduce the area of near saturated

region.

The result mapping function, f(x), is:







⎩

⎪

⎨

⎪

⎧



-1



xN

x in 0, N



N

N

ln

 x in N

, N



xb

-Asin

2π

x-N

 x in N

, 255

1) The curve starts from linear, x runs from 0 to

=max(ave

-det

, 0), with the curve output in

, r

], where r



OutMaxV

N





is the ratio

amplifying values in dark region from (N

=ave

+det

] to (r

, OutMaxV

2) To avoid oversaturation at the same time maintain

higher values in previous brighter part, OutMatV

is set to

min



N





, max



ave

*rmax

*1-r,160





and r in [0, 1] is set to

mindet

, det



maxdet

, det



;

3) The rest of the part connects the maximum output

of the second (OutMatV

) to the maximum of 8-

bit output 255. Then, r



255-OutMaxV

255-N



and



2551-r

. To supress the near saturated

4) region, the half period of a sinuous function is

subtracted, where T= 2(255-N

) is the period of

5) the sinuous function, A is the magnitude of the

sinuous function. In experiment, A is set to 20.

Curve based Fast Detail Enhancement for Biomedical Images

339

128

192

256

0 64 128 192 256

When ave

5, ave

det

130 and the

maximum value of dark region, OutMatV

, is set to

130, the curve is demonstrated in Figure 4. Apply the

mapping process to 8-bit colour will result in Figure

5(b), where the very dark and very bright pixels are

mapped into the middle value range.

Figure 4: Curve with offset = 5, dark/bright division = 130.

(a) 8-bit color

(b) 8-bit color after curve processing

Figure 5: Curve and histogram equalization applied to 8-bit

colours of gray (x, x, x), red (x, 0, 0), green (0, x, 0) and

blue (0, 0, x) in order of (R, G, B), where x = [0, 255].

The design of the intensity mapping function and the

threshold selection is based on the non-linear

perception

of eyes in brightness and colour to the

Figure 6: Grey scale 8-bit colour. From block (a) to block

(h), intensity is gradually increased from 0 to 255. In each

block, the intensity in each adjacent slice is increased by 4.

input pixel values. As the 8-bit gray scale colour bar

demonstrated in Figure 6, the perception of intensity

change varies in each group. Eyes are more sensitive

to the colour difference in the mid-dark range (32 –

224) but not in the near black (0 – 32) and near

saturated (224 – 255) range. Mapping the range of

image intensities to the range of eye sensitive region

will help eyes to perceive more details from the input.

After intensity mapping, global histogram

equalization will have wider range of input images

with different intensity distributions. i.e. high contrast

laparoscopic surgical frames with have peaks in dark

and/or bright regions or X-ray images with pixels

distributed in mid-range. As demonstrated in Figure

6, apply the algorithm to a high contrast laparoscopic

image, the intensity of dark region will be increased

to reveal more details and the amplification of

brightness part is supressed without saturation. Apply

the same strategy to X-ray image, dark and bright

pixels from the input will be more evenly distributed

to obtain better eye perceived contrast.

(a) Laparoscopic

frame

(b) histogram

(f) histogram

(e)

rocessed (a) (

)

rocessed (c) (h) histo

Figure 7: X-ray, laparoscopic surgical video frames (a, c) and their histogram (b, d) of intensity (V) calculated in HSV colour

space.

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4 EXPERIMENTAL RESULTS

To examine the algorithm, images from websites are

tested. Following criteria are selected to evaluate the

performance of the algorithm:

‐ Amount of details revealed before / after the

application

‐ The colour consistency and truthfulness

before and after the application

‐ Amount of noise, alien boundaries

introduced through the application of the

algorithm

‐ Perceived change in brightness

As demonstrated Figure 8, when applied to high

contrast laparoscopic surgical frames, details

including blood vessels and tissue patterns are better

presented at the same time maintained the colour

perception. Results of the algorithm applying to X-

ray images are similar to results of GHE with less

introduced noise level.

The algorithm is real-time capable for HD, 60fps,

1080*1920 video frames with the processing speed

approx. 8ms per frame, profiled through 4-core,

2.8GHz CPU. For similar size X-ray images, the

speed is around 3ms per image as only 1 channel is

processed, and no colour space conversion is

required.

5 CONCLUSIONS

This paper introduced a curve mapping and histogram

equalization-based method to enhance perceived

contrast and details of input biomedical images,

colour or gray-scale. The method takes advantages of

Ostu histogram analysis to adaptively separate

images into sub-regions with similar intensity

distributions, which intensively save the

computational loads. Curve based mapping adopted

from camera sensor processing map the image

intensity to eye sensitive range to help global

histogram equalization achieves better colour

truthfulness and reduce noise in near black region.

Original images Global Histogram

Equalization

Processed results

Figure 8: More results applied on website images.

Curve based Fast Detail Enhancement for Biomedical Images

341

Original images Global Histogram

Equalization

Processed results

Figure 8: More results applied on website images (cont.).

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ACKNOWLEDGEMENTS

We thank the R&D projects NBCP 2019C50052 and

NCHI I01200100023 for funding.

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APPENDIX

Table 1: Source of images (in display order of Figure 8).

https://www.youtu

be.com/watch?v=f

s_hJO1RZMs

Lap chole basic,

around 3:12

https://medtube.net/general-

surgery/medical-videos/24250-

laparoscopic-cholecystectomy-

with-mishra-knot

https://www.youtu

be.com/watch?v=

SpSNewRpdW0

Full length HD

Laparoscopic

Cholecystectomy

with Critical View,

around 3:44

https://www.youtube.com/watch?

v=O4pO_RXELvE

Single incision robotic

cholecystectomy, around 1:10

http://drkashi.scie

nce/?p=3211,

Cefuroxime as a

prophylactic

antibiotic in

laparoscopic

cholecystectomy

https://smallanimal.vethospital.uf

l.edu/clinical-services/internal-

medicine/endoscopy/abdominal-

endoscopy/, Abdominal

Endoscopy

World J Gastrointest Surg. Feb 27, 2019; 11(2): 62-84,

Figure 13

Voermans, Rogier P., et al. "Hybrid NOTES transgastric

cholecystectomy with reliable gastric closure: an animal

survival study." Surgical endoscopy 25.3 (2011): 728-

736. Figure 1

https://www.flickr.

com/photos/iem-

student/29110322

657

https://www.waybuilder.net/swee

thaven/MedTech/Dental/DentalR

ad/default.asp?iNum=0303

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