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\title{Machine Vision based Unsupervised Summarization of Wireless Capsule Endoscopy Video}
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\section[{INTRODUCTION}]{INTRODUCTION}\par
Medical endoscopy has developed into an important technique for minimally invasive surgery in recent years. Examination on multiple body areas and for minimally invasive abdominal surgery, joints and other body areas. The term "endoscopy" is derived from the Greek and describes a minimally invasive method of "looking inside" the human body. This is accomplished by inserting a medical device called an endoscope into a hollow organ or body cavity. Depending on the part of the body, it is inserted through a natural body opening or through a small incision that acts as an artificial access. Additional incisions are required during surgery to insert various surgical instruments.\par
Endoscopy is a catch-all phrase for a wide range of quite varied medical procedures. \hyperref[b0]{[1]} Endoscopy has numerous varieties, each of which has unique qualities. They can be categorised using a variety of standards, including ? Body part (e.g., abdomen, joints, gastrointestinal tract, lungs, chest, nose) ? Specialty in medicine (e.g., general surgery, gastroenterology, orthopaedic surgery) ? Therapeutic vs. diagnostic focus.\par
Wireless Capsule Endoscopy (WCE) is a unique form of endoscopy procedure that falls under the diagnostic focus. The patient must swallow a tiny capsule with a built-in camera that transmits a huge number of frames over a prolonged period of time to an external receiver. The doctor then evaluates the footage once this process is complete. WCE is essential for small intestine exams in particular because neither gastroscopy or colonoscopy can access this part of the gastrointestinal tract. 
\section[{London Journal of Medical and Health Research}]{London Journal of Medical and Health Research}\par
Wireless capsule endoscopy is typically used to examine the small intestine, which is difficult to reach with traditional endoscopy procedures. It is often used to diagnose conditions such as Crohn's disease, celiac disease, and tumours or ulcers in the small intestine. The procedure is non-invasive and painless, and most people are able to go about their daily activities while wearing the recording device.\par
Capsule endoscopy is a medical diagnostic tool that records video of a patient's digestive tract.\par
The method includes two devices. The first is a capsule with a camera and lights inside. The patient swallows this pill and sends its image to an external receiver. This external receiver is worn by the patient. Capsule endoscopy is used to capture images of the bowel that cannot be reached by conventional endoscopy methods. Capsule endoscopy is used to perform the following diagnostic procedures:\par
? Recognize inflammatory bowel illnesses; determine the cause of gastrointestinal bleeding. ? Identify cancer.\par
? Identify celiac illness.\par
? Look for polyps.\par
? After additional imaging examinations, perform follow-up testing. It is difficult to quickly extract the needed information from such a sizable video archive. Hence, methods are required to aid with the difficulty of managing video data. Video summarization is a fundamental method for handling video data.The sample frames from the capsule endoscopy procedure is depicted in  In order to extract the most important information, known as the keyframe of the video, video summarization attempts to limit the amount of duplicate data. It makes it possible for viewers to swiftly understand the key points of the video. It needs a thorough grasp of the video to produce a synopsis of it. Hence, it is hoped to create a framework that shows the viewer the useful components of video data by taking the information into account.\par
Video summary messages are typically run using two different approaches: static (keyframe-based) and dynamic (video hover-based) video analytics messages. The static video message contains a collection of a small but significant number of silent frames called keyframes, while the dynamic video message contains a collection of short important video clips. The video summary considers various characteristics such as representativeness, uniformity, static attention, temporal attention, and quality including hue, brightness, contrast, number of colours, edge distribution for keyframe selection. \hyperref[b1]{[2]} One of the major problems with capsule endoscopy procedures as mentioned above is that the gastro-intestinal video frames captured in the process are about 8 to 12 hours long. Reviewing the video frames is extremely time consuming for the physicians. The work's major objective is to shorten the time needed for reviewing videos of capsule endoscopy by pointing out the relevant portions of the video to the physician. To achieve this goal this, work aimed at generating a solution for extracting a strip of keyframes that can be presented as a summary of the original video.\par
With the intent of generating clusters that should be linked to the original video so that the user must be able to easily access the input video frames of every cluster, the two research questions addressed here are:\par
1. The process of summarising a lengthy capsule endoscopy video in a strip/timeline 2. The process of summarising groups of related frames by a single 'summary' 
\section[{II. RELATED WORK}]{II. RELATED WORK}\par
Due to the immense amount of video data being generated, there is an increased demand to analyse and summarise them. The work in the domain of video summarization has been carried out for a decade. For summarization, the videos will have to be sampled and divided into segments and shot boundaries. A technique for indexing and searching massive amounts of video data is called video summarization. To provide the user with a visual abstract of the video sequence, the approach outputs a brief summary of the video. A good video abstract aims to minimise noise while maximising the amount of information retained in the summary. Clustering techniques are frequently used for automatic video summarization, and they either extract a key-frame or a moving image for each cluster (video skims). In a clustering process, a cluster is a collection of objects that are more similar to one another than they are to the objects in the other clusters (groups). A good cluster will have minimal intercluster and intra-cluster variance.\par
The research study was focused on exploring the solutions available in literature for summarization of endoscopy procedures. The survey in this category consisted of perusing works from \hyperref[b2]{[3]} to \hyperref[b11]{[12]}.\par
Ismail et al., \hyperref[b2]{[3]} had proposed an unsupervised based approach for summarization of WCE videos. Here, the temporal descriptor as well as the colour and texture descriptors were used to represent each video frame. The possibilistic membership values and ideal feature weights inside each cluster were optimised by the authors using a probabilistic clustering and feature weighting technique with an objective function. A mean Jaccard coefficient of 15 had been attained using the technique. Chen et al., \hyperref[b3]{[4]} had proposed a Siamese Neural Network (SNN) approach and Support Vector Machine (SVM) for summarization of WCE videos. In this approach SNN was used to map. Similar frame pairings were mapped closer using SNN, whereas dissimilar image pairs were mapped farther apart in the feature space.\par
Euclidean distance measure was computed in order to detect shot boundaries. AN F-measure of 84.75 \% was achieved.\par
Emam et al., \hyperref[b4]{[5]}  In another work the authors Mehmood et al., \hyperref[b7]{[8]} proposed a technique to manage the data generated by WCE procedure. A binary classification approach was proposed to either discard or keep the frame based on colour space conversion, contrast enhancement and curvature measurement.\par
Lakshmi Priya \hyperref[b8]{[9]} had proposed a detection process that involved three steps: visual content representation for the feature extraction, construction of continuous signal for similarity assessment between the consecutive frames and the classification of continuity values for Transition identification. A central tendencybased shot boundary detection for video summarization was implemented here. Khan et al., \hyperref[b9]{[10]} had proposed an ensemble saliency model, consisting of motion, contrast, texture, and curvature saliency for summarization of WCE videos. Sainui \hyperref[b10]{[11]} had suggested a colour histogram feature and an optimisation method based on the quadratic mutual information statistical dependence measure for increasing coverage of the full video content and reducing redundancy among chosen key frames. For the purpose of summarising echocardiography films, domain-specific knowledge and automatic spatiotemporal structure analysis were integrated by Ebadollahi et al., \hyperref[b11]{[12]}. Using the graph that was generated, the videos were time sampled.\par
The survey gave an understanding that the existing solutions focused on extracting statistical features from the individual frames in the video for the purpose of summarization and keyframe extraction. The existing works have not considered the global features of the video and the temporality of the video for summarization purposes.\par
Further, these summarization London Journal of Medical and Health Research techniques were mostly focused on WCE procedure videos. 
\section[{III. IMPLEMENTATION}]{III. IMPLEMENTATION}\par
Implementation wise the algorithm designed is roughly divided into three parts. These three steps begin with the feature-distance calculation, clustering of the frames, and key-frame extraction from the clusters. The approach combines k-means and local maximum finding. The video frame distances are plotted on a graph. The chi-squared distances between the two-colour histograms of the frames are what these distances represent. The colour histograms have a 4x8x8 bin distribution and are in CIE lab colour space. Then, by looking for the local maxima, it establishes cluster boundaries. These local maxima are discovered with these characteristics. 1. The distance is the greatest distance found after comparing the distances of 2k symmetric neighbours. 2. The distance exceeds the second maximum by an amount n. The closest frame to the cluster mean is chosen by the algorithm to extract a key frame. The proposed algorithm consists of five steps that are depicted in a pipeline like shown in figure \hyperref[fig_4]{3}. The algorithm steps are given in Table  {\ref 1}. 
\section[{Table 1: Algorithm Steps for Key Frame Extraction}]{Table 1: Algorithm Steps for Key Frame Extraction}\par
Step 1: Creation of an array of colour histograms.\par
Step 2: Calculation of the distance between all consecutive frames and recording them in an array  Once stored, they can be reused when calculating keyframe fidelity and when changing settings.\par
Distance is the similarity metric between two frames that are considered in a cluster. The distances between all consecutive frames in the cluster is computed and stored in the array.\par
TUsing the Chi-squared test specified in equation 1, the distance between two successive frames is computed using the histograms for the CIE lab colour space.d (C1, C2) = ?(C1 -C2) 2 / C1 ???????????????.(\textbf{1})\par
Where C1 and C2 are colour histograms in CIE lab colour space as of two consecutive frames.\par
Clustering is a method of grouping data points in a data set based on their similarity. The algorithm used here is based on finding local maxima.\par
Grouping is done according to the distance table.\par
The algorithm covers all calculated distances. It will examine 2 k symmetrical neighbours at each distance point (k neighbours to the left and k neighbours to the right) and determine which neighbours have the highest value. A boundary is established between two points if the current distance is n times greater than the greatest distance between its neighbours (n1).\par
A key-frame acts as a representative frame that represents a cluster. The mean of the colour histogram is calculated and the frame closest in proximity to the mean histogram is chosen. The resulting key-frames from this technique were not of an optimal quality. The frame that was closest to the mean distance of the cluster's subsequent frames was chosen in the second iteration.\par
By contrasting a key-frame with the other frames in the cluster, the key-frame fidelity metric expresses the quality of a key-frame. The maximum 15 distances between the key frame and its cluster make up the key frame fidelity. The equation 2 given below is used to calculate the fidelity of the keyframe extracted fidelity (KeyFr k , Fr) = max i distance (Fr(i), KeyF k ) i = s k , ...e k ????????????. (\par
Where KeyFr k is the key frame of cluster k, Fr is the set of frames in the video, s k is the starting frame of the cluster k, e k the final frame of the cluster k. The distance function is used to calculate distance between two frames. Keyframe fidelity is calculated so that the user can see the quality of the keyframe relative to other generated keyframes. 
\section[{IV. RESULTS AND DISCUSSION}]{IV. RESULTS AND DISCUSSION}\par
For the experiments four test videos with various symptoms are considered from the publicly available dataset Kvasir-Capsule Dataset \hyperref[b12]{[13]}. The test videos have been resized to a resolution of 256x256. The frames are downsampled at 3 frames per second.\par
? Angiodysplasie: This video consists of 125608 frames.\par
? Bleeding: This video consists of 68939 frames.\par
? Polyp: This video consists of 124970 frames.\par
? Ulcer: This video consists of 121934 frames.\par
The symptoms in the test set videos considered are explained here. Angiodysplasie, also known as vascular malformation, refers to an abnormality in the blood vessels that can occur in various parts of the body, including the gastrointestinal tract, lungs, brain, and skin. In the gastrointestinal tract, angiodysplasia is a common cause of gastrointestinal bleeding, especially in older adults. It is characterised by the presence of small, dilated blood vessels in the mucosal lining of the intestines, which can rupture and cause bleeding.\par
A polyp is a growth that projects from the inner lining of a body organ. Polyps can occur in different parts of the body, including the colon, uterus, nasal passages, and stomach. An ulcer, also known as peptic ulcer, is an open sore that can develop in the lining of the stomach or small intestine. Helicobacter pylori (H. pylori). The sequence of a keyframe strip is shown in Figure \hyperref[fig_7]{4}.   The compression rate increases similarly to an exponential decay function as the parameters k and n are increased. The key-frame quality degrades roughly linearly as n and k increase. with a little bias in favour of the n-th parameter. This shows that the benefits of compression are diminishing in comparison to key-frame quality. 
\section[{V. CONCLUSION}]{V. CONCLUSION}\par
The endoscopy video is a special video domain having specific characteristics like specular light reflections, indistinct edges, occlusions, blurriness and artefacts like polyps, smoke, blood, or liquids.\par
The first two paragraphs can be merged into one Endoscopy videos are content that are unedited having highly similar information, in terms of colour and texture and no shot boundaries.\par
Endoscopy videos contain a lot of unimportant content like small segments where nothing important happens. It is important and necessary to mine this video content to extract relevant portions that require attention from the physician. This would save the physician's time to a great extent. In this work the spatial features of the video frame namely the histogram distribution in CIE colour space has been considered for key frame extraction. The key frame extraction pipeline has been designed and implemented and the quantity and the quality of the frames extracted have been assessed. Since the video data consists of both spatial and temporal features it is important to consider both of these features in order to generate more meaningful summaries. Further work in summarization of endoscopy video should consider both spatial and temporal features.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 2 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Step 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{fig_6}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 			\label{foot_0}\footnote{\label{foot_0} Machine Vision based Unsupervised Summarization of Wireless Capsule Endoscopy Video © 2023 Great ] Britain Journals Press} 			\label{foot_1}\footnote{\label{foot_1} Volume 23 | Issue 4 | Compilation 1.0 Machine Vision based Unsupervised Summarization of Wireless Capsule Endoscopy Video © 2023 Great ] Britain Journals Press} 			\label{foot_2}\footnote{\label{foot_2} © 2023 Great ] Britain Journals Press} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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