Cervical cancer is a major cause of mortality among women worldwide, and early detection is crucial for successful treatment. However, the interpretation of cervical whole slice images can be challenging due to poor image quality. This paper presents a study on the use of histogram techniques to enhance the quality of cervical whole slice images. The aim of the study is to improve the visibility of important structures in the image, such as blood vessels and cell nuclei, for more accurate diagnosis and treatment of cervical cancer. The study used histogram equalization and stretching techniques to enhance the contrast and brightness of cervical whole slice images. Experiments were conducted to test the effectiveness of these techniques in improving the image quality. The results show that the enhanced images are of higher quality and are easier to interpret than the original images. The histogram equalization technique improved the visibility of structures in the image by increasing the contrast, while the histogram stretching technique improved the brightness and color balance. In conclusion, this study demonstrates the effectiveness of histogram techniques in enhancing the quality of cervical whole slice images for better diagnosis and treatment of cervical cancer. The use of these techniques can greatly improve the visibility of important structures in the image and lead to more accurate diagnosis and treatment. These findings can have important implications for the development of more effective screening and diagnostic methods for cervical cancer.

1.
W.A.
Mustafa
, and
M.M.M.A.
Kader
, “
Contrast Enhancement Based on Fusion Method: A Review
,”
J. Phys. Conf. Ser.
1019
(
012025
),
1
7
(
2018
).
2.
Win,
Kyi
Pyar
,
Yuttana
Kitjaidure
,
Kazuhiko
Hamamoto
, and
Thet Myo
Aung
.
"Computer-assisted screening for cervical cancer using digital image processing of pap smear images
."
Applied Sciences
10
, no.
5
(
2020
):
1800
.
3.
Gautam
,
Srishti
,
Nirmal
Jith
,
Anil K.
Sao
,
Arnav
Bhavsar
, and
Adarsh
Natarajan
.
"Considerations for a PAP smear image analysis system with CNN features
." arXiv preprint arXiv:1806.09025 (
2018
).
4.
William
,
Wasswa
,
Andrew
Ware
,
Annabella Habinka
Basaza-Ejiri
, and
Johnes
Obungoloch
.
"Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm
."
Informatics in Medicine Unlocked
14
,
23
33
(
2014
).
5.
Sun
,
Guanglu
,
Shaobo
Li
,
Yanzhen
Cao
, and
Fei
Lang
.
"Cervical cancer diagnosis based on random forest
."
International Journal of Performability Engineering
13
, no.
4
,
446
(
2017
).
6.
Rundo
,
Leonardo
,
Andrea
Tangherloni
,
Marco S.
Nobile
,
Carmelo
Militello
,
Daniela
Besozzi
,
Giancarlo
Mauri
, and
Paolo
Cazzaniga
.
"MedGA: a novel evolutionary method for image enhancement in medical imaging systems
."
Expert Systems with Applications
119
, pp.
387
399
(
2019
).
7.
Cheng
,
Shenghua
,
Sibo
Liu
,
Jingya
Yu
,
Gong
Rao
,
Yuwei
Xiao
,
Wei
Han
,
Wenjie
Zhu
et al.
"Robust whole slide image analysis for cervical cancer screening using deep learning
."
Nature communications
12
, no.
1
,
5639
(
2021
).
8.
Gonzalez Rafael
C.
,
Woods
,
Richards
E.
:
Digital Image Processing
.
Pearson Prentice Hall
2006
,
1
(
1
):
142
165
.
9.
Zuiderveld
,
Karel
. "Contrast Limited Adaptive Histograph Equalization."
Graphic Gems IV. San Diego: Academic Press Professional
, pp.
474–485
(
1994
)
10.
K.
Singh
,
R.
Kapoor
,
Image enhancement using exposure based sub image histogram equalization Pattern Recogn
.
Lett.
,
36
, pp.
10
14
(
2014
)
11.
Singh
K.
,
Kapoor
R.
,
Sinha
S.K.
Enhancement of low exposure images via recursive histogram equalization algorithms
.
Optik.
126
:
2619
2625
(
2015
).
12.
Kim
Y-T.
Contrast enhancement using brightness preserving bi-histogram equalization
.
IEEE Trans. Consum. Electron.
43
, pp.
1
8
(
1997
).
13.
Yu
Wang
,
Qian
Chen
and
Baeomin
Zhang
,
"Image enhancement based on equal area dualistic sub-image histogram equalization method
," in
IEEE Transactions on Consumer Electronics
, vol.
45
, no.
1
, pp.
68
75
(
1999
).
14.
Sonka
,
M.
,
Hlavac
,
V.
, &
Boyle
,
R.
(
2014
). “
Image processing, analysis, and machine vision
”.
Cengage Learning.
15.
G.
Senthamaraiand
K.
Santhi
,”
Dynamic multi histogram equalization for image contrast enhancement with improved brightness preservation
,”
In: IEEE 2nd International Conference on Electronics and Communications Systems (ICECS)
,
2015
.
16.
Ziad M.
Abood
,
Falah A.
Bid
, (
2017
). Enhancement the brightness and contrast of the image medical by numerical equations.
Conference 23 J. of Education College
,
Mustansiriyah University
.
17.
Agarwal
M.
,
Mahajan
R.
Medical image contrast enhancement using range limited weighted histogram equalization
.
Proc. Comput. Sci.
125
, pp.
149
156
(
2018
).
18.
Agaian
S.S.
,
Panetta
K.
,
Grigoryan
A.M.
Transform-based image enhancement algorithms with performance measure
.
IEEE Trans. Image Process.
10
, pp.
367
382
(
2001
).
19.
Zhou
Wang
and
A. C.
Bovik
, “
A universal image quality index
,” in
IEEE Signal Processing Letters
, vol.
9
, no.
3
,pp.
81
84
, March
2002
,
20.
Mean Squared Error
. In:
Sammut
,
C.
,
Webb
,
G.I.
(eds)
Encyclopedia of Machine Learning
.
Springer
,
Boston, MA
. (
2011
)
21.
Y. K.
Eugene
and
R.G.
Johnston
, “
The Ineffectiveness of the Correlation Coefficient for Image Comparisons
”,
Technical Report LAUR-96-2474, Los Alamos
,
1996
.
22.
Hussain
,
Elima
(
2019
), “
Liquid based cytology pap smear images for multi-class diagnosis of cervical cancer
”,
Mendeley Data, V4.
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