Shape Recognition in High-level Image Representations:

Data Preparation and Framework of Recognition Method

Jagoda Lazarek and Piotr S. Szczepaniak

Institute of Information Technology, Lodz University of Technology, Lodz, Poland

Keywords:

Image Processing, Pattern Recognition, Image Representation, Graph Grammars, Mammograms, Spiculated

Masses.

Abstract:

The automatic shape recognition is an important task in various image processing applications, including

medical problems. Choosing the right image representation is key to the recognition process. In the paper,

we focused on high-level image representation (using line segments), thanks to which the amount of data nec-

essary for processing in subsequent stages is signiﬁcantly reduced. We present the framework of recognition

method with the use of graph grammars.

1 INTRODUCTION

Since image processing and image analysis are key in

decision supporting systems and process automatisa-

tion, there is a need to develop processing techniques.

As a complex multistage process consisting of seg-

mentation, transformation, extraction of features, and

pattern classiﬁcation, it is necessary to choose the best

technique for each of them, which results in the suc-

cess of the ﬁnal recognition.

First of all, it is important to preapare data –

choosing the right representation of the image, de-

pending on the task. The algorithms can be based on

a digital image in an unprocessed version called ma-

trix (pixel) representation (low-level representation)

or any other high-level representation, e.g., using line

segments or edges. The last of these (high-level rep-

resentation) is particularly adequate to problems in

which the structure of recognized objects is impor-

tant. In the paper we discuss data preparation process

and framework of recognition method.

As important as the selection of image representa-

tion is the use of an appropriate recognition method.

Among the methods of image recognition, three ap-

proaches can be distinguished: statistical pattern

recognition (Chen, 1973; Devijver and Kittler, 1982;

Fukunaga, 1972; Fukunaga, 1990; Kurzynski, 1997;

Schurmann, 1996; Vapnik, 1998; Webb and Copsey,

2011), syntactic pattern recognition (Bunke and San-

feliu, 2000; Fu, 1982; Gonzales and Thomason, 1978;

Miclet, 1986; Pavlidis, 1977; Skomorowski, 2013)

and neural pattern recognition (Dunne, 2007; Omid-

var and Dayhoff, 1997; Pao, 1989; Schurmann, 1996).

Solving problems in which the image structure

is important requires a syntactic approach. The im-

age is decomposed into primary components (sim-

ple elements) whose mutual relations build its struc-

ture. The syntactic approach includes string, tree and

graph methods. Images are represented respectively

by means of string, tree and graph grammars. Image

recognition consists in performing a parsing (syntac-

tic analysis) that determines whether graph is accept-

able, correct for a deﬁned grammar.

Based on the above observations, we proposed

a framework for shape recognition in images in high-

level representations using graph grammars. The

choice of graph grammars is due to their greater de-

scriptive power – in comparison with string or tree

grammars. The descriptive power of graph grammars

gives more options in solving complex problems, but

at the same time is a challenge in the process of ana-

lyzing them.

2 DATA PREPARATION:

HIGH-LEVEL IMAGE

REPRESENTATIONS

As mentioned in the Introduction, image analysis re-

quires the interpretation of a huge amount of low-level

data – pixels. Proper data preparation is key.

Two stages can be distinguished in the process of

image preparation for recognition: preprocessing and

Lazarek, J. and Szczepaniak, P.

Shape Recognition in High-level Image Representations: Data Preparation and Framework of Recognition Method.

DOI: 10.5220/0007579000570064

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 57-64

ISBN: 978-989-758-353-7

data reduction. Preprocessing methods are used to

eliminate noise and unnecessary information, includ-

ing changing the colour model, mathematical opera-

tions on images, improving contrast, removing arte-

facts, and identifying areas of interest (Malina and

Smiatacz, 2008).

Reducing the amount of data can be achieved us-

ing the methods of detecting lines, edges and segmen-

tation (Ballard and Brown, 1982). It allows ﬁnding

consistent, in terms of speciﬁc criteria, areas – consti-

tuting objects or their fragments. In principle, three

groups of methods are distinguished: methods of re-

gion growing, clustering and detection of boundaries.

So far, a wide variety of methods have been

developed to detect edges and lines (Forsyth and

Ponce, 2003; Gonzalez and Woods, 2008; Pratt, 1991;

Shapiro and Stockman, 2001; Sonka et al., 2007). The

edge detection methods are the ﬁrst step in the meth-

ods of detecting lines. Many of the edge detection

methods use convolution masks and differential oper-

ators. Among the known edge detection methods, one

should mention the Roberts operator, the Sobel op-

erator, the Prewitz operator, the Laplacian operators,

Kirsch and Robinson masks. More advanced meth-

ods include the following algorithms: Marr-Hildreth

(1980) (Marr and Hildreth, 1980), Canny (1986)

(Canny, 1986), Boie-Cox (1986/87) (Boie and Cox,

1987), Shen-Castan (1992) (Shen and Castan, 1992),

Frei-Chen (1970) (Frei and Chen, 1977). One of the

well-known methods is the Hough transform (Gonza-

lez and Woods, 2008; Sonka et al., 2007). In 2013,

Krylov and Nelson proposed a method to detect line

segments and curvilinear structures, (Krylov and Nel-

son, 2014; Krylov et al., 2013). Another line segment

detector LSD was developed in 2014 by Grompone

von Goi (Grompone von Goi, 2014).

Based on detected complex structures such as

lines, shapes, it is possible to change the represen-

tation of the image from low-level to high-level rep-

resentation.

By default, the image is represented by pixels, and

more precisely by a rectangular pixel grid. Such rep-

resentation is not natural for a human who perceives

more complex objects – lines, shapes, spots. In ad-

dition, image analysis in the low-level – pixel repre-

sentation requires the analysis of a very large num-

ber of data. Hence, it is justiﬁed to distinguish in

the picture signiﬁcant structures, objects – coherent

in terms of certain criteria, e.g . level of grey, colour,

texture. This process is very important because it al-

lows further image processing at a level higher than

pixel (Umbaugh, 2011) (ﬁgure 1). High-level repre-

sentations can be based on e.g.:

• line segments or edge segments (Grompone von

Goi, 2014; Krylov and Nelson, 2014; Lazarek and

Szczepaniak, 2014; Lazarek et al., 2014),

• superpixels (Achanta et al., 2012),

• contours – ,,active partitions” (Pryczek et al.,

2010; Tomczyk et al., 2012),

• OB (ang. Object Bank) (Li et al., 2010; Li et al.,

2014).

The higher the image representation level, the

greater the knowledge about it, and the number of data

necessary for processing decreases.

Figure 1: Visualization of the change of representation of

the image from low-level (pixels) to high-level representa-

tion (line segments).

Usage of context information is presented in ﬁg-

ure 1 – analysis of adjacent pixels allows detection

of more complex structures. Instead of describing the

image using the values associated with each pixel in-

dependently, it is possible to use the description us-

ing e.g., the coordinates of the ends of the segments,

which increases the knowledge about the image being

processed.

3 HIGH-LEVEL IMAGE

REPRESENTATION –

EXAMPLE: MAMMOGRAM

IMAGE

The selection of the right representation is closely re-

lated to the problem being solved. The concept of the

use of high-level image representation is presented on

the example of mammogram data preparation for the

task of recognizing spiculated changes.

3.1 High-level Image Representation

Using Line Segments

Spiculated changes are characteristic masses that can

be observed on mammography images. Spiculated

change consists of a bright centre and star-shaped ex-

tensions of spicules spreading from it. An example

of a spiculated change is shown in ﬁgure 2. An oc-

currence of spiculated changes is a strong premise of

breast cancer (Kopans, 2007).

BIOIMAGING 2019 - 6th International Conference on Bioimaging

Figure 2: Spiculated change in mammogram.

By analyzing ﬁgure 2 it can be observed that

bright bands radiate from the bright centre – the

spicules, that can be represented by line segments.

Therefore, for the purpose of analysis of mammo-

graphic images for the presence of spiculated lesions

on them, it was proposed to change the representa-

tion of the image from low-level (pixels) to the rep-

resentation of a higher level (line segments). In order

to detect the segments of the line, the Krylov-Nelson

method was used. The image in such a representation

can be used to further analysis, enabling the detec-

tion and recognition of spiculated changes. Figure 3

presents a fragment of a mammogram image with de-

tected line segments that create its new representation

(high-level).

The automatic detection of spiculated changes,

despite signiﬁcant research in this area, remains

a challenge (Jiang et al., 2008). This is due to the fact

that spiculated changes are often very subtle and are

characterized by a large variety in appearance (Jiang

et al., 2008), the number of spicules depending on the

case may vary enormously.

3.2 Selection of a Signiﬁcant Part of the

Image – ROI

In order to prepare data for the recognition process, it

is necessary to extract interesting fragments from an

image – which may contain a spiculated change.

In the considered task, the choice of the ROI (area

of interest) can be realized in two ways – automatic

or supervised.

Regardless of the way ROI is selected, the square

area is extracted and then the coordinates of its centre

(intersection diagonals) are determined – (c

, c

). In

ﬁgure 4, the ROI containing the spiculated change is

Figure 3: Spiculated change with detected line segments

(with the use of the Krylov-Nelson method).

Figure 4: The area of interest – ROI (with highlighted sub-

areas) including the spiculated change visible in the mam-

mogram image. Initial ROI position selected in a supervised

way.

selected.

After determining the ROI in the high-level im-

age representation, it can be seen that the ROI frame

intersects the found structures – groups of line seg-

ments, marked with the same colour as in ﬁgure 4.

Omitting the line segments belonging to the separated

structures but remaining outside the ROI would re-

sult in a loss of both the number and the quality of

the data. To solve this problem, an original solution

was proposed to create a ”ﬂexible” ROI frame that

adapts its shape to the groups of founded line seg-

ments. According to the author’s knowledge, the pro-

posed method of expanding the area of interest (ROI)

has not been described in the literature.

In ﬁgure 4 the selected area (ROI) is divided into

Shape Recognition in High-level Image Representations: Data Preparation and Framework of Recognition Method

four sub-areas. It can be observed that some of de-

tected groups of line segments (marked with the same

colour) cross the boundaries of the area of interest, or

are simultaneously in two adjacent sub-areas:

• a green group of line segments lying partly in the

right upper ROI sub-area, and partly outside it,

• a blue group of line segments lying partly in the

right, lower ROI sub-area, and partly outside it,

• yellow group of line segments lying partly in the

right, lower ROI sub-area, and partly outside it,

• a red group of line segments lying partially in the

left, lower sub-area of the ROI, partly in the right,

lower sub-area of the ROI and - partly outside the

ROI area,

• a purple group of line segments lying partially in

the left, lower ROI sub-area, and partly outside it,

• a blue group of line segments lying partially in the

left upper ROI sub-area, and partly outside it.

Omitting these fragments of groups of line segments

that lie outside the boundaries of the selected ROI

would result in the loss of a signiﬁcant part of the in-

formation, and thus affect the effectiveness of the di-

agnosis. Similarly to ignoring information about the

belonging of detected line segments to larger groups,

i.e. only a geometric interpretation of the position of

line segments, which does not take into account the

line segments belonging to complex structures (lo-

cated in several sub-areas).

To avoid losing information, an innovative solu-

tion was proposed to match the ROI’s area to the in-

formation in the image. Each of the four sub-areas

expands independently of the others, adapting to the

information in the image. Systems of groups of line

segments are included in those sub-areas in which

they are in the majority. Three main steps may be

listed:

• segments are initially assigned to sub-area, in

which lays at least one of its ends;

• segments are reorganized – segments are assigned

to sub-area, in which the majority of segments

from the same group lay;

• new bounding boxes for sub-areas are created –

the smallest rectangle which contains all segments

from sub-area is drawn.

The extended original ROI frame shown in ﬁgure 4 is

shown in ﬁgure 5.

3.3 Graph Construction

Due to the structural nature of spiculated changes, we

proposed to recognize them using graph grammars.

Figure 5: Flexible ROI frame - built of four segments.

The framework of the method is presented in the next

subsection.

To recognize spiculated changes using graph

grammars, it is necessary to construct a graph rep-

resenting the area of interest. Graph consists of ﬁve

vertices – one of them is a previously designated point

that is the centre of the original ROI, the other four are

associated with four sub-areas of the extended ROI.

The vertex in the centre of ROI is assigned the label

s, while the labels for the remaining vertices are as-

signed in – depending on the arrangement of line seg-

ments located in the given sub-area of the ROI. The

drawing 6 illustrates the concept. The red line seg-

ments represent the dominant direction of the line seg-

ments in a given sub-area (black), are determined on

the basis of the median of directions of all segments

located in a given sub-area. The selected segments are

assigned the appropriate labels in accordance with the

principles presented in ﬁgure 7. Subsequent vertices

are indexed according to the principles presented in

Figure 6: ROI with 4 sub-areas with detected line segments

(black) and red line segments – being a symbolic represen-

tation of the dominant line segments direction in a given

segment.

BIOIMAGING 2019 - 6th International Conference on Bioimaging

ﬁgure 8. The next step is the proper connection of the

vertices – the edge comes out from the vertex with the

smaller index and goes to the vertex with the larger

index. Next, a label describing relations between ver-

tices is assigned to each edge. A two-element set of

edge labels was deﬁned – Γ = {r, t}:

• r – is assigned to the edge from a central vertex to

each non-central vertex,

• t – is assigned to the edges connecting non-central

vertices.

Figure 7: Visualization of a set of labels for dominant line

segments ∆ = {s, a, b, c, d}.

Figure 8: Graph representation of the pattern shown in ﬁg-

ure 6 with assigned vertex labels and vertex indexes.

Figure 9: Graph representation of the pattern shown in ﬁg-

ure 6 with assigned vertex labels, vertex indexes, and edge

labels.

Graph representation of the pattern shown in ﬁg-

ure 5 (mammogram) is presented in ﬁgure 10.

Patterns presented in ﬁgure 9 and 10 may be rec-

ognized with dedicated graph grammar. Whats con-

sists one of the stages of graph grammar based shape

recognition method described in the next section.

Figure 10: Graph representation of the pattern shown in ﬁg-

ure 5 (mammogram) with assigned vertex labels, vertex in-

dexes, and edge labels.

4 GRAPH GRAMMAR BASED

SHAPE RECOGNITION

METHOD

The method of semantic image analysis in high-level

representation (e.g., line segments) with the use of

graph grammars enables the detection and recognition

of a selected class of objects. A universal processing

sequence has been proposed that can be adapted to

the task of detection and recognition of any class of

objects that can be described using dedicated graph

grammars. The image processing scheme is shown in

ﬁgure 11.

4.1 Graph Grammars

Graph representations and graph grammars are used

in many areas of practical importance, e.g.:

• representation of the logical structure of algo-

rithms (Nagl, 1979),

• deﬁning the semantics of programming languages

(Gottler, 1983),

• code optimization (Nagl, 1979),

• modeling and processing of databases (Angles

and Gutierrez, 2005; Angles and Gutierrez, 2008;

Cheng et al., 2009; Nagl, 1979),

• information processing (Jiang and Bunke, 2017),

• system modeling (Kotulski and Sedziwy, 2011;

Kotulski and Szpyrka, 2011; Rafe et al., 2009;

Sedziwy et al., 2012; Szpyrka and Kotulski, 2011;

Szpyrka et al., 2017),

• deﬁning visual languages (Ehrig et al., 1999; Her-

mann et al., 2008; Rekers and Schurr, 1997;

Zhang et al., 2001).

Shape Recognition in High-level Image Representations: Data Preparation and Framework of Recognition Method

The important ﬁeld being of interest in this pa-

per is computer image analysis (Flasinski, 1989;

Flasinski, 2007; Flasinski and Myslinski, 2010; Fu,

1974; Gonzales and Thomason, 1978; Lazarek and

Szczepaniak, 2016; Lin et al., 2009; Pavlidis, 1977;

Rosenfeld, 1976; Shaw, 1969; Tadeusiewicz and

Flasinski, 1991).

Graph grammars are commonly used to describe

images, but rarely to recognize. The reason for

this situation is due to the computational complex-

ity of the syntactic analysis process (Skomorowski,

2013). However, graph grammars are a very interest-

ing tool for creating recognition systems in which ob-

ject classes are deﬁned explicitly. Due to the known

problem related to the computational complexity of

the syntactic analysis algorithms, it is particularly im-

portant to select the appropriate class of graph gram-

mars to ensure effective syntactic analysis. Grammat-

ical graphs that have these features are ETPL class

grammars (k) (embedding transformation preserved

production ordered, k-left nodes unambiguous). Their

effective syntactic analysis is possible, whose compu-

tational complexity is O(n

) (Flasinski, 1989; Flasin-

ski, 2007; Skomorowski, 2013).

4.2 Method of Semantic Image Analysis

in High-level Image Representation

The method of semantic image analysis in high-level

representation (e.g., line segments) with the use of

graph grammars enables recognition of a selected

class of objects. A processing sequence has been pro-

posed that can be adapted to the task of recognition of

any class of objects that can be described using dedi-

cated graph grammars.

The method requires two main steps – changing

the representation of the image (to high-level) and de-

signing the appropriate graph grammar. Processing

schema (ﬁgure 11) is as follows:

1. Changing the representation of the image from

pixel to the representation of a higher level (e.g.,

line segments).

2. Detection of ROI – regions of interest.

3. Representation of ROI using a graph.

4. Graph analysis with the use of dedicated graph

grammar to recognize the object in the image.

5 SUMMARY

In the paper, we have shown the importance of prepa-

ration data process, which demands to change image

Figure 11: Processing scheme for the method of semantic

image analysis in high-level representation with the use of

graph grammars.

representation from low-level to high-level represen-

tation. Choosing proper representation strongly de-

pends on recognition purpose. We have depicted that

such representation may be used for the creation of

object structure and its further recognition with the

use of graph grammars, what was explained in the

section 4 about the framework of recognition method.

The presented general framework of graph gram-

mar based shape recognition method was successfully

used for recognition of spiculated masses in mammo-

graphic images where the high-level image represen-

tation (line segments) and dedicated graph grammar

were applied for recognition purpose (Lazarek, 2017).

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BIOIMAGING 2019 - 6th International Conference on Bioimaging