Deep Features Extraction for Endoscopic Image Matching

Houda Chaabouni-Chouayakh

1,2

, Manel Farhat

1,2

and Achraf Ben-Hamadou

1,2

Centre de Recherche en Num

erique de Sfax, 3021, Sfax, Tunisia

Laboratory of Signals, Systems, Artiﬁcial Intelligence and Networks (SM@RT), Sfax, Tunisia

Keywords:

Endoscopic Images,Deep Learning, Image Feature Matching, Adaptive Triplet Loss.

Abstract:

Image feature matching is a key step in creating endoscopic mosaics of the bladder inner walls, which help

urologists in lesion detection and patient follow-up. Endoscopic images, on the other hand, are particularly

difﬁcult to match because they are weekly textured and have limited surface area per frame. Deep learning

techniques have recently gained popularity in a variety of computer vision tasks. The ability of convolutional

neural networks (CNNs) to learn rich and optimal features contributes to the success of these methods. In this

paper, we present a novel deep learning based approach for endoscopic image matching. Instead of standard

handcrafted image descriptors, we designed a CNN to extract feature vector from local interest points. We pro-

pose an efﬁcient approach to train our CNN without manually annotated data. We proposed an adaptive triplet

loss which has the advantage of improving the inter-class separability as well as the inter class compactness.

The training dataset is automatically constructed, each sample is a triplet of patches: an anchor, one positive

sample (a perspective transformation of the anchor) and one negative sample. The obtained experimental re-

sults show at the end of the training step a more discriminative space representation where the anchor becomes

closer to the positive sample and farther from the negative one in the embedding space. Comparison with the

well-known standard hand-crafted descriptor SIFT in terms of recall and precision showed the effectiveness

of the proposed approach, reaching the top recall value for a precision value of 0.97.

1 INTRODUCTION

Endoscopy is a widely used clinical procedure for the

early detection of numerous cancers (e.g., nasopha-

ryngeal, oesophageal adenocarcinoma, gastric, col-

orectal cancers, bladder cancer, etc.) and therapeutic

procedures (e.g., polypectomy, injection sclerother-

apy, variceal banding, etc.). An endoscope is the in-

strument used during an endoscopic exploration. It

could be a rigid or ﬂexible thin tube equipped by a

lighting source and a camera. The doctor visualizes

the captured stream of video endoscopy while explor-

ing the inner surfaces of hollow organs.

Nevertheless, endoscopy has several limitations

due mainly to the limited ﬁeld of view and reduced

maneuverability inside the bladder. Indeed, anoma-

lies are usually spread over areas larger than the ﬁeld

of view making lesion partitions laid out over many

frames in the endoscopic video. This makes organs

exploration and anomalies detection time consuming

and difﬁcult for the doctors since they should fol-

low the spatial distribution of the lesions over several

frames. In addition, the ability to ﬁnd structure simi-

larities between images taken from different views or

during different clinical examinations is a tough task.

To address this issue, it is a common practice to re-

place the clinical endoscopic video with a panoramic

image generated by stitching the video frames in a

common coordinate system that covers a wide region

of interest. For the generation of panoramic images,

feature matching and ﬁnding correspondence between

images is a key step.

Because of the large variability in texture and

appearance aspect of the endoscopic images be-

tween patients, developing a robust and accurate fea-

ture matching between endoscopic images is partic-

ularly difﬁcult. So far, this matching problem has

been addressed by either directly computing classi-

cal similarity measures between the images (e.g., mu-

tual information, cross-correlation, etc.) or by us-

ing hand-crafted features such as SIFT(Lowe, 2004),

SURF(Bay et al., 2008), etc. The main advantages

of such algorithms are the simplicity and the efﬁ-

ciency. The basic idea in this case is to ﬁrst ex-

tract features for a set detected image interest points.

After that, point matching between images is per-

Chaabouni-Chouayakh, H., Farhat, M. and Ben-Hamadou, A.

Deep Features Extraction for Endoscopic Image Matching.

DOI: 10.5220/0010833700003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

925-932

ISBN: 978-989-758-555-5; ISSN: 2184-4321

925

formed based on the distance between their feature

vectors. This category of techniques has previously

been used to stitch images for various applications in-

cluding remote sensing (Ait-Aoudia et al., 2012), un-

derwater navigation (Elibol et al., 2017), retina anal-

ysis (Hossein-Nejad and Nasri, 2018), and endoscopy

(Behrens et al., 2009; Bergen et al., 2013). In the case

of endoscopic images, we can cite for instance the

works of (Weibel et al., 2010; Ben-Hamadou et al.,

2016; Daul et al., 2010; Behrens et al., 2010; Du

et al., 2011). In particular, (Behrens et al., 2010) and

(Du et al., 2011) propose respectively a SURF fea-

ture descriptor for real-time bladder mosaicing in ﬂu-

orescence endoscopy and a SIFT-based descriptor for

endoscopic zone matching. Recently, feature extrac-

tion from endoscopic images has been addressed in

the work of (Martinez et al., 2020) where color and

texture features are extracted and then exploited by

classical classiﬁers such as a random forest tree or a

KNN ensemble model for the classiﬁcation of kidney

stone captured with ureteroscopes. Later, the authors

showed in (Lopez et al., 2021) how CNN-based solu-

tions can further improve the classiﬁcation of kidney

stones, even when using a moderate number of im-

ages.

Nowadays, deep learning techniques clearly out-

perform standard and hand-crafted techniques for a

wide range of computer vision tasks including face

recognition (Lai et al., 2019), artifacts classiﬁcation

(Ali et al., 2021), image feature matching (Liu et al.,

2018), denoising (Zou et al., 2019), classiﬁcation

(Kim et al., 2021; Liu et al., 2020), and segmentation

(Ghosh et al., 2018). When it comes to endoscopy, to

the best of our knowledge, no previous study has been

proposed to build local features for endoscopic image

matching based on deep learning techniques.

In this context, we propose in this paper a new

deep learning-based method for learning local fea-

tures to improve endoscopic image matching. We de-

signed a CNN to transform local patches around im-

age interest points instead of applying standard hand-

crafted descriptors. The proposed training scheme

of the CNN does not require manual annotation of

data. We rather automatically construct a dataset of

image patches triplets. Each triplet consists of an an-

chor patch, one positive patch (which is a perspective

transformation of the anchor) and one negative patch.

During the training process, the network parameters

are trained with an adaptive triplet loss and updated

so that for a given training triplet, the anchor-positive

distance is minimized, while the anchor-negative dis-

tance is maximized. The learned CNN features are

then used to classify the dataset and estimate the

inter-class separability and intra-class compactness.

(128,128,1)

(124,124,16)

conv (5x5)

ReLU

MaxPool

(2x2)

(62,62,16)

(1,1,2048)

FCL 1

ReLU

(1,1,1024)

(1,1,512)

(1,1,128)

Output

Input

FCL 2

ReLU

FCL 3

ReLU

FCL 4

ReLU

Figure 1: Proposed CNN architecture. For a given 128 ×

128 patch, a 128-dimensional feature vector is provided in

the embedding space.

A comparison with the classical SIFT descriptor is

carried out in terms of precision/recall as in (Miko-

lajczyk and Schmid, 2005) to evaluate the efﬁciency

of the learned CNN features.

The organization of this paper is as follows: Sec-

tion 2 describes the proposed approach. Experimen-

tal results are presented and discussed in section 3.

Finally, section 4 provides conclusions and future

works.

2 PROPOSED APPROACH

This section details the proposed CNN architecture

as well as training steps that lead to a discriminative

feature space to describe all extracted patches around

image interest points. The main idea consists then in

training the proposed CNN with a set of patch triplets

in order to optimize an adaptive triplet loss that en-

courages similar patches to be closer in the feature

space. Comes after the feature matching step which is

achieved via computation of Euclidean distances be-

tween features in the embedding space.

2.1 CNN Architecture

The CNN proposed in this work is applied on input

patches of size 128 × 128. Figure 1 details the archi-

tecture of the proposed CNN. First, a 2D convolution

followed by a ReLU activation layer is applied to the

input data. Comes after a (2 × 2) max-pooling. Af-

ter that, 4 Fully Connected Layers (FCLs) intercepted

with ReLU activation layers are used. Finally, a nor-

malization of the data has been carried on. This archi-

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

926

triplet loss

embedding

space

CNN

triplet patches

anchor

positive

negative

TRAINING

triplet loss

Figure 2: Triplet loss training approach. Features extracted from anchor, positive and negative samples are fed to the triplet

loss and trained so that the anchor-positive distance is minimized and the anchor-negative one is maximized in the feature

space. A more discriminative embedding space is thus obtained.

tecture provides at the end a 128- dimensional feature

vector.

2.2 CNN Training

The generation of the training dataset is explained as

follows: First, the raw data is converted from color

to gray-scale images. The contrast of the images is

then enhanced. After that, image interest points are

detected without loss of generality via SIFT detec-

tor. Anchor patches are gathered by cropping 128 by

128 pixels around the detected image interest points.

It is noticeable that organ surfaces are typically im-

aged from various angles and perspectives. In or-

der to get the corresponding positive patches, we ap-

ply pseudo randomly generated perspective transfor-

mations on the set of collected anchor patches. In

fact, such transformations could simulate well typi-

cal angles/perspective changes in endoscopic videos.

The obtained positive patches have the advantage to

appear very similar to anchor patches. The nega-

tive ones, on the other hand, are chosen randomly

from the rest of the anchors and positive samples of

other triplets. Figure 3 shows some examples of con-

structed patch triplets.

Depending on the desired prediction task, many

loss functions have been proposed in the literature.

In this work, we opted for the triplet loss to train

our model. Triplet loss, introduced in (Schroff et al.,

2015) as FaceNet model, has been successfully used

in several tasks (Vygon and Mikhaylovskiy, 2021;

Kumar et al., 2021). It has the advantage to enforce

the inter-class separability as well as the intra-class

compactness. The main idea consists in comparing

a baseline (anchor) input to a positive (truthy) input

and a negative (falsy) input. During the optimization

Sample 1 Sample 2

Anchor

Positive

Negative

Sample 3

Figure 3: Three samples (i.e., sample 1, 2 and 3) of col-

lected triplet patches. An anchor is a patch cropped around

an image interest point, already detected without loss of

generality via SIFT detector. While the positive sample is

a perspective transformation of the respective anchor, the

negative one is chosen randomly from the rest of the an-

chors and positive samples of another triplet.

process, the network parameters are updated so that

the anchor-positive input distance is minimized, while

the anchor-negative input distance is maximized. Fig-

ure 2 illustrates the complete principle of the adaptive

triplet loss.

The triplet loss proposed in this work is deﬁned by

the following equation:

L =

∑

a,p,n∈{A,P,N}

(k f (a) − f (p)k

− k f (a) − f (n)k

+ α

a,p

)

(1)

Deep Features Extraction for Endoscopic Image Matching

927

where {A, P, N} denotes the triplets set: A, P and N

are respectively the anchor, positive and negative in-

puts. k.k

is the Euclidean distance. The function f (.)

stands for the feature embedding function that maps a

patch in the Euclidean embedding space via the CNN

architecture illustrated in Figure 1. Minimizing L en-

forces the maximization of the Euclidean distance be-

tween patches from different classes (anchor and neg-

ative) which should be greater than the distance be-

tween anchor and positive features in the embedding

space. In other words, the optimization process aims

at updating the different network parameters so that

the patches A and P become closer while A and N are

further apart in the Euclidean embedding space.

The term α

a,p

in Eq.(1) refers to a dynamic mar-

gin. It is designed as follows:

a,p

k f (a) − f (p)k

. (2)

In this work, we have noticed experimentally that the

use of adaptive margin is more advantageous than the

ﬁxed ones.

It is worth to note that adaptive margins have been

used in many applications such as person reidentiﬁ-

cation (Li et al., 2018), image retrieval (Zhao et al.,

2019). Promising results were obtained showing that

adaptive triplet loss encourages the learned image em-

bedding models to generalize well on cross-domain

data.

2.3 Feature Matching

The step after feature extraction with the trained CNN

is feature matching where the Euclidean distance be-

tween patches P

and P

t+1

cropped from two consec-

utive frames is computed in the embedding space ac-

cording to the following equation:

D( f (P

t+1

), f (P

)) =

f (P

t+1

) − f (P

)

(3)

This deﬁnition transforms ﬁnding patch matching

problem into a nearest neighbor search problem in the

Euclidean space. In this work, we utilized Nearest

Neighbor with a Threshold (NNT) to match patches

in which matching pairs is identiﬁed if the distance is

smaller than a certain threshold

. Note that, thresh-

olding based matching is commonly used to evaluate

descriptor performances (Lee and Park, 2014).

In our case, we set experimentally the NNT threshold

to 5.

3 EXPERIMENTAL RESULTS

AND DISCUSSIONS

We ran a series of experiments to evaluate the per-

formance of the proposed endoscopic image match-

ing in terms of True/False image interest point match-

ing. We additionally compared the obtained results to

SIFT descriptor, one of the well known state-of-the-

art hand-crafted image descriptors.

3.1 Dataset Construction

To evaluate the proposed model, we collected a set

of 300 image interest points from clinical patient en-

doscopic data with very different appearances. After

that, positive and negative patches are constructed as

already explained in 2. Figure 4 illustrates the dataset

construction process.

3.2 CNN Training

When applying the CNN architecture proposed in this

paper to the dataset described previously, the loss dis-

played in Figure 5 has been obtained, showing the

convergence of the model. The data has been trained

with a batch size of 36 using Stochastic Gradient De-

scent with an initial learning rate of 0.001 and a mo-

mentum set of 0.9. To avoid the over ﬁtting of the

model to the selected patch triplets, we update the

training dataset each 50 epochs. The total number of

epochs is 250. The total number of triplets is over

15000 triplets.

3.3 Obtained Results

The proposed model is evaluated in terms of:

• Discriminative ability of the obtained embedding

space or in other words the representativeness of

the generated features.

• Robustness to typical angles/perspective changes

in endoscopic videos.

3.3.1 Discriminative Ability of the Embedding

Space

In order to measure the ability of the proposed method

to generate a discriminative embedding space rep-

resentation of the dataset, we deﬁne three types of

triplets; hard, semi-hard, and easy samples. The de-

gree of easiness/ hardness is expressed in terms of

inter-class separability and intra-class compactness.

More Details about the different sample types are

given bellow:

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

928

Figure 4: After conversion to gray-scale level and contrast

enhancement, image interest points are detected. Anchors

are obtained by cropping 128 by 128 pixels around the de-

tected image interest points. While positive patches are per-

spective transformation of the corresponding anchors (via

T (.)), the negative ones are selected randomly from the rest

of anchors and positives.

• Hard Samples: in this case, the samples will

be matched as false positives since the anchor-

positive distance is greater than the anchor-

negative one.

k f (a) − f (p)k

> k f (a) − f (n)k

• Semi-hard Samples: in this case, the anchor-

positive distance is still smaller than the anchor-

negative one but becomes larger when adding the

Figure 5: Convergence of the loss of the proposed approach.

An update of the training dataset is carried on every 50

epochs to avoid the over ﬁtting. We notice that the loss

increases systematically on each dataset update but starts

again to decrease.

adaptive margin. Formally:

k f (a) − f (p)k

+ α

a,p

> k f (a) − f (n)k

• Easy Samples: these samples should also be

called safe samples. In here, it is easy to sepa-

rate the negative patch. In face, these samples are

characterized by the fact that the anchor-positive

distance remains always smaller than the anchor-

negative one even when adding the adaptive mar-

gin. Mathematically speaking:

k f (a) − f (p)k

+ α

a,p

< k f (a) − f (n)k

From the deﬁnitions given above, it is clear that the

larger the proportion of easy samples is, the more dis-

criminative the embedding space is, and the respec-

tively the larger the proportion of the hard samples is,

the less discriminative the embedding space is.

During the training process we monitor the pro-

portion (or percentage) of each of the three triplet

types. The idea is to check if during the training the

obtained feature space allows to increase the propor-

tion of easy samples at the expense of semi-hard and

hard samples, resulting thus in a more discriminative

feature space. Figure 6 summarizes the updates of the

easy-to-hard partition of the triplet samples during the

training step.

As depicted in Figure 6, we could notice that

the semi-hard samples proportion decreases dramat-

ically, while the easy samples one gets higher, reach-

ing more than 90% at the end of the training process.

This means that through the optimization process, the

different network parameters are updated so that the

learned features transfer samples from the hard, semi-

hard packages to the easy one, where the matching be-

tween the anchor and positive patches becomes more

intuitive.

Deep Features Extraction for Endoscopic Image Matching

929

Percentage %

Epochs

Figure 6: Easy-to-hard partition of the triplet samples. We

observe that over training epochs a more discriminative

space representation is estimated allowing the an easy sep-

aration between positive and negative samples. The propor-

tion of easy samples increases (reaching more than 90%) at

the expense of semi-hard and hard samples (together less

than 10%).

3.3.2 Robustness of the Model to Viewpoint

Changes

When using an endoscope, organ surfaces are typi-

cally imaged from various angles and perspectives,

resulting in a stream of video endoscopy. There-

fore, we evaluate the performance of our approach in

matching interest points in the same endoscopic video

in terms of robustness against viewpoint changes. We

also compare it to the well-known standard hand-

crafted descriptor SIFT.

To simulate typical viewpoint changes in endo-

scopic videos, we ﬁrst detect image interest points and

then estimate homographies from real clinical data

between consecutive frames based on RANSAC al-

gorithm (Fischler and Bolles, 1981).

Ten sequences of human bladder endoscopic

videos from different patients have been used for this

purpose. Each sequence consists of thousands of

frames with a wide range of texture, luminosity, and

appearance. Homologous points matching between

consecutive frames has been manually performed to

collect the matching ground truth. Once homogra-

phies have been estimated, we randomly apply them

to image sequences. For each pair of images ( i.e.,

original and warped images), image interest points are

detected. Then, point matching is performed twice,

using our method and SIFT method. Evaluation is

carried out qualitatively and quantitatively to compare

the two methods.

Figure 7 depicts the qualitative evaluation of the

SIFT descriptor and the proposed one when match-

ing two endoscopic frames. Green and red lines re-

(a) SIFT matching.

(b) Our matching.

Figure 7: Qualitative evaluation of the matching perfor-

mances carried out between two endoscopic frames. Im-

age interest points are colored in blue. Green and red lines

refer respectively to correct and wrong matches. The best

matching quality is reached with our approach.

fer respectively to correct and wrong matches. Com-

pared to SIFT, we can observe that a larger number

of correct matches (green lines) is achieved with our

approach.

1-Precision

0.6 5

0.7 0

0.7 5

0.8 0

0.8 5

0.9 0

0.9 5

1.0 0

Recall

Precision-Recall Curv es w it h NNT

Prop osed SIFT

Figure 8: Quantitative evaluation: Recall/precision com-

puted for SIFT and proposed approach when varying view-

point angles. The proposed descriptor outperforms SIFT,

reaching the maximal recall value for a precision value of

0.97.

For quantitative evaluation, we compare our

learned features matching performance to the SIFT

one in terms of recall with regard to precision. The

obtained results are depicted in Figure 8. We can

see that our method outperforms SIFT reaching the

top recall value for a precision value of 0.97. An-

other advantage of the method over hand-crafted ap-

proaches such as SIFT is that the proposed training

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

930

scheme does not require manual annotation, allowing

for additional few steps of ﬁne tuning without manual

intervention for a better adaptation of the CNN model

for the sequence speciﬁcity without loss of generality

of the approach.

4 CONCLUSIONS AND FUTURE

WORKS

In this work, we propose the ﬁrst deep learning

based approach for endoscopic image matching. For

this purpose, a triplet-based dataset of image patches

triplet has been ﬁrstly constructed. After that, to train

the CNN, an adaptive triplet loss has been designed to

improve the inter-class separability as well as the inter

class compactness leading to a discriminative feature

space. We assessed the robustness of the proposed ap-

proach against viewpoint changes and compared the

obtained performances to SIFT, one of the most suc-

cessful state-of-art descriptors.

Our further work will be focused on exploring

graph neural networks to integrate neighboring image

interest points in order to improve the discriminative

ability of the model. We intend also to test our ap-

proach on other endoscopic data captured for different

organs.

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