Kazim Ali1, Faheem Abbas 2, Atif Iqbal3 1Kazim Ali,

Kazim
Ali1, Faheem Abbas 2, Atif Iqbal3

 

1Kazim Ali, PhD Student, IT Department,
University of Central Punjab Lahore

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[email protected]

2Faheem Hussain, MS Student,
IT Department, University
of Central Punjab Lahore

[email protected]

3Atif Iqbal, MS Student, IT Department,
University of Central Punjab Lahore

[email protected]

 

ABSTRACT

The
segmentation and detection of nuclei in Histopathology images is very important
in these days to indentify different diseases like cancer etc. A large variety
of approaches for segmentation of nuclei in breast cancer histopathology images
have been proposed. Digital pathology
represents one of the major evolutions in modern medicine. Pathological
examinations constitute the

gold standard in many medical protocols, and also
play a critical and legal role in the diagnosis process. In the conventional
cancer

diagnosis, pathologists analyze biopsies to make
diagnostic and prognostic assessments, mainly based on the cell morphology and

architecture distribution. They
vary not only in the core segmentation methods, but also in the pre- and post
processing steps that aim to improve segmentation performance. We will give an
overview of image analysis for the sake of nuclei segmentation and detection.
In this paper, we will use the concept of blob detection in digital image
processing by using three digital image processing algorithms which are
Laplacian of Gaussian (Log), Difference of Gaussian (Dog) and Determinant of
Hessian (Hog). This paper proposes a method of nuclei segmentation and
detection which purely based on image processing techniques. At the end of
paper we will compare results of these three algorithms on a cancer patient’s
dataset.

 

Keywords—
Segmentaq   2w`3tion, Log, Dog, Doh, Blod
detection.

 

INTRODUCTION

Breast
cancer is one of the most common cancer along with lung and bronchus cancer,
prostate cancer, colon cancer, and pancreatic cancer among others. Recently, computerized methods have been
rapidly evolving in the area of digital pathology, with growing applications
related to nuclei detection, segmentation, and classification. In cancer
research, these approaches have played, and will continue to play a key  role in minimizing human intervention,
consolidating pertinent second opinions, and providing traceable clinical
information. Pathological studies have been conducted for numerous cancer
detection and grading applications, including brain, breast, cervix, lung, and
prostate cancer grading.

Breast
cancer is the most prevalent form of cancers among women and image analysis
methods that target this disease have a huge potential to reduce the workload
in a typical pathology lab and to improve the quality of the interpretation 1.  This study investigated blob detection and
blob parameter estimation. Estimated blob parameters have many potential
applications, such as noise filtering and medical image analysis. Many
applications may benefit from these blob parameters, because they produce
simpler or more accurate algorithms 2. The utilization of data science and
machine learning approaches in medical fields proves to be prolific as such
approaches may be considered of great assistance in the decision making process
of medical practitioners. With an unfortunate increasing trend of breast cancer
cases comes also a big deal of data which is of significant use in furthering
clinical and medical research, and much more to the application of data science
and machine learning in the aforementioned domain 3.  Pathological examination, in which a series of
H-stained histopathological slides are manually examined by pathologists
for disease diagnosis, is a time-consuming and labor-intensive task. More
importantly, this process is subjective, prone to error, and has large inter-
and intra observer variation. Due to the heterogeneity and morphological
complexity of tumors, it is a challenging task even for well-trained
pathologists to reach an agreement when diagnosing a tumor sample by visual
inspection of H-stained images 45.

 

Related
Work

1
The procedure can be divided into four main steps: 1) preprocessing with color
un mixing and morphological operators, 2) marker-controlled watershed
segmentation at multiple scales and with different markers, 3) post-processing
for rejection of false regions and 4) merging of the results from multiple
scales. The procedure was developed on a set of 21 breast cancer cases (subset
A) and tested on a separate validation set of 18 cases (subset B). The
evaluation was done in terms of both detection accuracy (sensitivity and
positive predictive value) and segmentation accuracy (Dice coefficient). 6 Starts with an overview of the tissue
preparation, staining and slide digitization processes followed by a discussion
of the different image processing techniques and applications, ranging from
analysis of tissue staining to computer-aided diagnosis, and prognosis of
breast cancer patients. 7 Proposes a method uses two observations,
above. First, real E-only images exhibit lower contrast when compared to
digitally separate E-only images using the wedge-finding method and second, the optical density of
some regions of the H image is significantly higher than the sum of the
optical densities of the corresponding Honly and E-only image. 2 Detects
image blobs and estimated parameters using the determinant of the Hessian
operator. To investigate differential detectors quantitatively, a mathematical
function was used to represent the blobs and to solve the parameters, including
the position, width, length, contrast, offset, and orientation, in a closed
form. 8 Compares six machine learning (ML) algorithms: GRU-SVM, Linear
Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Soft max
Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast
Cancer (WDBC) dataset by measuring their classification test accuracy and their
sensitivity and specificity values. 3 Presents, discusses, and extracts the major trends from an exhaustive
overview of various nuclei detection, segmentation, feature computation, and
classification techniques used in histopathology imagery, specifically in
hematoxylin–eosin and immunohistochemical staining protocols. 5 Presents
an automated cell nuclei segmentation approach that works with H-stained
images. A color de-convolution algorithm was first applied to the image to get
the hematoxylin channel. Using a morphological operation and thresholding
technique on the hematoxylin channel image, candidate target nuclei and
background regions were detected, which were then used as markers for a
marker-controlled watershed transform segmentation algorithm. Moreover, post processing
was conducted to split the touching nuclei. For each segmented region from the
previous steps, the regional maximum value positions were identified as
potential nuclei centers. These maximum values were further grouped into
k-clusters, and the locations within each cluster were connected with the
minimum spanning tree technique. Then, these connected positions were utilized
as new markers for a watershed segmentation approach. The final number of
nuclei at each region was determined by minimizing an objective function that
iterated all of the possible k-values. The proposed method was applied to the
pathological images of the tumor tissues from The Cancer Genome Atlas study.
Experimental results show that the proposed method can lead to promising
results in terms of segmentation accuracy and separation of touching nuclei.
9 Detection of nuclei can be accomplished by using a CNN as a classifier and
applying sliding window through the whole image. The result will be a
probability map which indicates the probability of each pixel to be the
centroid of a nucleus. A CNN classifier consists of two parts: (i) the feature
extraction part that includes a few convolution layers followed by pooling
layers and an activation function such as a sigmoid, tanh, and ReLU; and (ii)
the classification part which is a few fully connected layers complemented by a
loss function. 10 presents a robust
and accurate novel method for segmenting cell nuclei using a combination of
ideas. The image foreground is extracted automatically using a graph cuts based
binarization. Next, nuclear seed points are detected by a novel method combining
multi-scale Laplacian of Gaussian filtering constrained by distance map based
adaptive scale selection. These points are used to perform an initial
segmentation that is refined using a second graph cuts based algorithm
incorporating the method of alpha expansions and graph coloring to reduce computational
complexity. 11 Proposes a novel segmentation algorithm for detecting
individual nuclei from Hematoxylin and Eosin (H) stained breast
histopathology images. This detection framework estimates a nuclei saliency map
using tensor voting followed by boundary extraction of the nuclei on the saliency
map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF).
The method was tested on both whole-slide images and frames of breast cancer
histopathology images.

 

Proposed
Method

The
proposed method is based on blob detection technique which is used in digital
image processing to detect stars and galaxies in satellite images. But we are
trying to use this technique to detect nuclei in breast cancer images. The
proposed method based on this technique is given by

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fig.
1

The
programming language and libraries which are used in the proposed method to
detect nuclei are given under.

Python Lanuage
Scikit-image: It
is an open source image processing library for the Python programming
language. It includes algorithms for segmentation, geometric
transformations, color space manipulation, analysis, filtering,
morphology, feature detection, and more.
Numpy:  It is used to array processing for numbers,
strings, records, and objects).
Matplotlib: It is
a plotting library for the Python programming language and its numerical
mathematics extension NumPy.

The image processing algorithms which are used in this work
are given below.

3.1
Laplacian of Gassian:
Laplacian filters are derivative
filters used to find areas of rapid change (edges) in images. Since derivative
filters are very sensitive to noise, it is common to smooth the image (e.g.,
using a Gaussian filter) before applying the Laplacian. This two-step process
is called the Laplacian of Gaussian (LoG) operation. The mathmatical equation
is:

3.2
Difference of Gaussian:
The DOG performs edge detection by performing a Gaussian blur on
an image at a specified theta (also known as sigma or standard deviation). The
resulting image is a blurred version of the source image. The module then
performs another blur with a sharper theta that blurs the image less than
previously. The final image is then calculated by replacing each pixel with the
difference between the two blurred images. The equation is given.

3.3 Determinant of Hessian: The
Hessian blob detector 42, 43 is based on a 2 × 2 matrix of second-order derivatives of
image intensity I(x, y), called the Hessian matrix. This matrix can be used
to analyze local image structures and it is expressed in the form

 

H(x, y, ?) =

where Ixx, Ixy, and Iyy
are second-order image derivatives computed using Gaussian function of
standard deviation ?. In order to detect interest features, it searches
for a subset of points where the derivatives responses are high in two
orthogonal directions. That is, the detector searches for points where the
determinant of the Hessian matrix has local maxima.

                                  

                          
det(H) = IxxIyy – I2
xy

 

3.4 Blob Detection Algorithm
Analysis:

LoG is the most accurate and slowest
approach. It computes the Laplacian of Gaussian images with successively
increasing standard deviation and stacks them up in a cube. Blobs are local maxima
in this cube. Detecting larger blobs is especially slower because of larger
kernel sizes during convolution. Only bright blobs on dark backgrounds are
detected.

DoG
is a faster approximation of LoG approach. In this case the image is blurred
with increasing standard deviations and the differences between two
successively blurred images are stacked up in a cube. This method suffers from
the same disadvantage as LoG approach for detecting larger blobs. Blobs are
again assumed to be bright on dark.

DoH
is the fastest approach. It detects blobs by finding maxima in the matrix of
the Determinant of Hessian of the image. The detection speed is independent of
the size of blobs as internally the implementation uses box filters instead of
convolutions. Bright on dark as well as dark on bright blobs is detected. The
downside is that small blobs (<3px) are not detected accurately. 4.       Dataset The proposed methodology was tested and evaluated on de-identified and de-linked images of histopathology specimens from the Department of Pathology, Christian Medical College Hospital (CMC), the proposed method was validated on eight representative images of H stained breast cancer histopathology sections. 5.       Result The resultant output of LoG, DoG and Doh algorithms are given below.                                         Fig (a). Original image Fig(b). after applying LoG algorithm                 Fig(c). After applying DoG         Fig(d). After applying DoH   5.1 Performance Evaluation Metrics: The following metrics are used for performance evaluation. Recall=R = Tp / ( Tp+ Tn) Precision=P = Tp / (Tp + Fp ) Where True Positive (TP): correctly identified nuclei. False Positive (FP): incorrectly identified non nuclei. True Negative (TN): correctly rejected. False Negative (FN): incorrectly rejected nuclei. Table no. 1 Algorithms Manual Count of  Nuclei Count of Nuclei by Proposed Method Recall Precision   LoG   12115   11886   98%   100%   DoG   12115   3850   31%   100%   DoH     12115     11213   92%   100%   Conclusion In this work we have used three algorithms, LoG, DoG and HoG for nuclei detection in cancer image dataset. The LoG achieve 98% recall and 100% precision, the DoG achieves 31% recall and 100% precision and, the DoH achieves 92% recall and 100% precision. According to above results, it is proved that the performance of LoG and HoG is much better than DoG. So DoG algorithm is not efficient, we cannot use it in this specific scenario. We only talk about the perfomance LoG and DoH. According table No.1 results LoG is slightly better than DoH. After a literature review we find the DoG is costly than DoH. Therefor in our point of view about these two algorithms is (i) LoG will be used when we have more computational power and required more and more accuracy (ii) the DoH will be better in the environment where less computational power is available but when we can compromise on slightly low accuracy.   REFERENCES         1         Breast Cancer Histopathology Image analysis by Mitko Veta.      2 Blob Detectczzion with the Determinant of the Hessian by       Xiaopeng Xu. Computer and Information Engineering College, Inner Mongolia Normal University, Hohhot, 010022, China.      3 Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential. Humayun Irshad, Student Member, IEEE, Antoine Veillard, Ludovic Roux, and Daniel Racoceanu, Member, IEEE. IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 7, 2014. 4 Convolutional Neural Networks for Biomedical Image Analysis, Alex Kalinin, PhD Candidate DCM, University of Michigan June 1, 2017. 5  Faliu Yi, Junzhou Huang, Lin Yang, Yang Xie, uanghua Xiao, "Automatic extraction of cell nuclei from H&E-stained histopathological images," J. Med. Imag. 4(2), 027502 (2017), doi: 10.1117/1.JMI.4.2.027502. 6 Breast Cancer Histopathology Image Analysis: A Review Mitko Veta, Josien P. W. Pluim, Paul J. van Diest, and Max A. Viergever. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 5, MAY 2014. 7 ALGORITHM AND BENCHMARK DATASET FOR STAIN SEPARATION IN HISTOLOGY IMAGES. Michael T. McCann, Joshita Majumdar, Cheng Peng, Carlos A. Castro, Jelena Kova. Magee-Womens Research Institute and Foundation of the University of Pittsburgh, Pittsburgh, PA, USA. 8 On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset Abien Fred M. Agarap. Department of Computer Science Adamson University Manila, Philippines. arXiv:1711.07831v1 cs.LG 20 Nov 2017. 9  Detection of Nuclei in H Stained Sections Using Convolutional Neural Networks Mina Khoshdeli, Richard Congy, and Bahram Parvin, Biomedical and Electrical Engineering Department, University of Nevada, Reno, NV, U.S.A,  Amador Valley High School, Pleasanton, Ca, U.S.A. 10 Improved Automatic Detection & Segmentation of Cell Nuclei in Histopathology Images Yousef Al-Kofahi, Wiem Lassoued, William Lee, and Badrinath Roysam, Senior Member, IEEE. 11 Automated Segmentation of Nuclei in Breast Cancer Histopathology Images, Maqlin Paramanandam, Michael O'Byrne, Bidisha Ghosh, Joy John Mammen, Marie Therese Manipadam, RobinsonThamburaj, Vikram Pakrashi. 12 http://bioimage.ucsb.edu research/bio-segmentation /.  

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