A DSP-BASED ACTIVE CONTOUR MODEL

Juan Zapata

Depto. de Electr

onica, Tecnolog

ıa de Computadoras y Proyectos.Universidad Polit

ecnica de Cartagena.

Campus Muralla del Mar s/n. Cartagena, Spain

Ram

on Ruiz

Depto. de Electr

onica, Tecnolog

ıa de Computadoras y Proyectos.Universidad Polit

ecnica de Cartagena.

Campus Muralla del Mar s/n. Cartagena, Spain

Keywords:

Ultrasound image processing, DSP processors, active deformable contours.

Abstract:

In this paper a DSP-based active contour model for tracking of the endocardium in a sequence of echocar-

diographic images is presented. If a contour is available in the ﬁrst frame of a sequence, the contours in the

subsequent frames are segmented. Deformable active contours is a technique that combine geometry, physics

and approximation theory in order to solve problems of fundamental importance to medical image analysis;

such as segmentation, representation and matching of shapes, and the tracking of objects in movement. The

procedure has been developed on a DSP processor using its hardware features. The results are illustrated using

a sequence of four-chambers apical echocardiographic images.

1 INTRODUCTION

In recent years, ultrasound image processing has at-

tracted the attention of researchers in the study of

diagnostic systems based on the analysis of images,

mainly due to the non-ionizing nature of ultrasonic ra-

diation and the consequent reduction of risks for the

patient and the medical professional. In this respect,

it can be conﬁrmed that some aspects of techniques

used in the analysis of images have been strongly in-

ﬂuenced by the development of solutions to typical

problems in this domain such as the recognition of

areas with anatomical meaning in the image and non-

rigid movement tracking.

Operations developed for image processing are

computationally intensives, mainly due to the tremen-

dous volume of optical information and the com-

plex mathematical operations required. This aspect

of computational demand becomes decisive when a

real-time result is required. General purpose proces-

sors are not appropriate for real-time medical process-

ing imaging applications because of technical trade-

offs in hardware design. General purpose processors

are extremely capable when it comes to data manip-

ulation but are inefﬁcients for mathematical calcula-

tion. DSP (digital signal processing) processors work

in an opposite way since they are efﬁcient for mathe-

matical calculation and less efﬁcients in data manip-

ulation (Lapsley et al., 1996). Digital signal process-

ing is one of the most powerful technologies and has

shown enormous growth and had an important tech-

nological impact in several areas, e.g. in telecommu-

nications, medical imaging, radar and sonar, high ﬁ-

delity music reproduction, to name just a few. DSP

systems are perfectly capable of solving the problem

of endocardium motion tracking in echocardiography

images.

Numerous papers have considered the problem of

automatic contour tracking. One approach is to use

active contour models to track the boundaries of

anatomic structures in medical images. This tech-

nique is a suitable alternative to classic edge-based

segmentation techniques, and combines the detection

of grey level transitions within the image together

with the need to obtain a closed contour. This task

involves both hardware and software aspects. In fact,

while purely hardware solutions have proved to be

inefﬁcient and unsuitable for complex algorithms, a

purely software solution based on a PC does not of-

fer a large bandwidth in terms of MOPS and system

throughput (Geminami et al., 1999). Therefore, an in-

tegrated hardware/software platform based on a DSP

processor located in a host PC was chosen as environ-

ment to develop active contour algorithms. Due to its

full programmability, high performance and novel ar-

chitecture, the TMS320C6701 is one of the most suit-

ables devices for researching and testing new digital

signal processing algorithms.

425

Zapata J. and Ruiz R. (2004).

A DSP-BASED ACTIVE CONTOUR MODEL.

In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 425-429

DOI: 10.5220/0001135604250429

 SciTePress

2 A CONTOUR TRACKING

ALGORITHM

Geometrically, a snake or deformable active contour

is a spline with a parametric representation v(s) =

(x(s), y(s)) embedded in the image plane (x, y ∈ R),

where x and y are the coordinate function and s ∈

[0, 1] is the parametric domain (Kass et al., 1988). En-

ergetically, the active contour is subordinated to two

types of energy that dictate its behaviour. Equation (1)

can be viewed as a representation of the energy of the

contour and the ﬁnal shape corresponds to the mini-

mum of this energy.

∗

snake

int

(v(s)) + E

ext

(v(s)) (1)

The ﬁrst term of the functional is the internal de-

formation energy. This internal energy (2) consists of

the ﬁrst and second derivatives of v(s) in relation to s

and characterizes the deformation of a stretchy, ﬂex-

ible contour. Two parametric functions, ω

(s) and

(s), dictate the simulated physical characteristics

of the tension and rigidity of the contour, respectively.

int

(v(s)) ds =

(s)

∂v(s)

∂s

+ ω

(s)

∂

v(s)

∂s

(2)

The second term, the external energy, also termed the

image energy, couples the snake to the image. Tradi-

tionally,

ext

(v(s)) ds =

P (v(s)) + E

con

(v(s)) ds

(3)

where P (v(s)) denotes a scalar potential function de-

ﬁned on the image plane. This scalar function can

be designed so that local minima coincide with inten-

sity extrema, edges or other image features of interest.

The second term of the equation (3), E

con

(v(s)), is

the energy consequence of external constraints. These

asserted constraints can beneﬁt certain zones or lo-

cal characteristics of the image. Therefore, during the

minimization procedure, the contour can be favoured

to reach these zones.

There are two key difﬁculties with the original

technique. First, the procedure requires that the ini-

tial contour lies near the feature of interest and relies

on an inherent force to move the contour towards the

edges. Cohen (Cohen, 1991) decided to include of

a force to oblige the active contour to expand and to

contract in the absence of other type of forces in or-

der to solve some of the problems encountered in the

original model, where main problem was that a snake

which is not close enough to the edges or interest fea-

tures is not attracted by them, and so the object con-

tour is reached very slowly or converges to a straight

line. The model developed by Cohen allows a be-

haviour like a balloon since the curve is driven by an

internal pressure force. In summary, our model pro-

poses to use this pressure force in order to allow a

faster approximation of an active contour when it is

not near the real contours of the object to segment.

Cohen formulates the balloon model in the following

form: F = k

~n(s) − k

∇P

||∇P ||

, where ~n(s) is a nor-

mal unitary vector to the curve at point v(s), k

is the

force amplitude and k a scale factor and P is the im-

age potential. Other methods have been proposed to

solve the problem of initialization (Leroy et al., 1996;

Cohen and Cohen, 1993). The main idea, common

in others techniques too, is to increment the capture

range of the external forces and to guide the initial

contour towards the desired contour.

A second problem appears when the active contour

cannot progress in the concavities of the object (Da-

vatzikos and Prince, 1995). There is no success-

full solution to this problem, although several forces

have been proposed by researchers (Cohen, 1991; Da-

vatzikos and Prince, 1995; Davatzikos and Prince,

1994; Prince and Xu, 1996). However, although

many of these proposed methods solve theproblem

they given rise to new difﬁculties. For example, pres-

sure forces must be initialized to push out or push in,

a condition that involves careful initialization. Xu and

Prince designed a new external force called gradient

vector ﬂow force or GVF force (Xu and Prince, 1997;

Xu and Prince, 1998); which is intended to solve the

aforementioned problems. The gradient vector ﬂow

ﬁeld is a vectorial ﬁeld which is derived from the

image through the minimization of an energy func-

tional. This external GVF force is computed as a dif-

fusion of the gradient vectors of a gray-level or binary

edge map derived from the image. The GVF ﬁeld can

be found by solving the following Euler equations,

where ∇

is the Laplacian operator:

µ∇

u − (u − f

)(f

+ f

) = 0 (4)

µ∇

v − (v − f

)(f

+ f

) = 0 (5)

Equations (4) and (5) can be rewritten as follows:

(x, y, t) =

µ∇

u(x, y, t) − b(x, y)u(x, y, t) + c

(x, y)

(x, y, t) =

µ∇

v(x, y, t) − b(x, y)v(x, y, t) + c

(x, y)

(6)

where:

b(x, y) = f

(x, y)

+ f

(x, y)

(7)

(x, y) = b(x, y)f

(x, y) (8)

(x, y) = b(x, y)f

(x, y) (9)

We have introduced a hybrid energy term (S

anchez

et al., 2000), which is an adaptive combination of both

ICINCO 2004 - ROBOTICS AND AUTOMATION

426

terms, GVF and balloon energy. In this way, if there is

no GVF ﬁeld, then the balloon force acts on the active

contour, although its inﬂuence over the active contour

decreases as the GVF force increases in magnitude.

The superposition of effects get through the balloon

force and the GVF force is ruled by a user parame-

ter. The optimum value of this parameter is obtained

when the inﬂuence of the pressure force over the ac-

tive contour is null.

3 HARDWARE OVERVIEW

3.1 TMS320C60 EVM evaluation

board

The choice of DSP-based hardware permits the de-

velopment of a powerful and highly ﬂexible sys-

tem. Due to its full programmability and high per-

formance, the TMS320C6701 processor mounted on

the TMS320C60 EVM is a suitable device for re-

searching and testing DSP algorithms. In the work

described in this paper, a personal computer was

equipped with the Code Composer Studio develop-

ment environment which helps to construct and debug

embedded real-time DSP applications. It provides

tools for conﬁguring, building, debugging, tracing

and analyzing programs. Texas Instruments DSP’s

provide on-chip emulation support that enables Code

Composer Studio to control program execution and

monitor real-time program activity. The heart of the

TMS320C60 EVM evaluation board is the Texas In-

struments TMS320C6701 processor. The C6701 is

based on a VLIW-like architecture which allows it to

execute up to eight RISC-like instructions per clock

cycle.

The two data paths of the C6701 extend the func-

tionality of the data paths of the C6201 with support

for 64-bit data and IEEE-754 32-bit single-precision

and 64-bit double-precision ﬂoating-point arithmetic.

Each data path includes a set of four execution units,

a general-purpose register ﬁle, and paths for moving

data between memory and registers. The four exe-

cution units in each data path comprise two ALUs,

a multiplier and an adder/subtractor which is used

for address generation. The ALUs support both in-

teger and ﬂoating point operations, and the multipli-

ers can perform both 16x16-bit and 32x32-bit integer

multiplies and 32-bit and 64-bit ﬂoating point mul-

tiplies. The two register ﬁles each contain sixteen

32-bit general-purpose registers. To support 64-bit

ﬂoating point arithmetic, pairs of adjacent registers

can be used to hold 64-bit data. In addition to the

operations supported by the C6201, the C6701 of-

fers support for ﬂoating-point reciprocal and recip-

rocal square root estimation, and for converting data

between ﬁxed- and ﬂoating-point formats.

3.2 Implementation

Although the code running on the DSP processor can

be written in C because the compiler generates an

quasi-efﬁcient code, the performance of the applica-

tion can be maximized by using compiler options,

intrinsic instructions, and assembly code transforma-

tion. In fact, this previous strategy was used in our

code.

The computational cost of the active contour algo-

rithm constitutes most the computational cost of the

entire procedure. Therefore, the algorithm is imple-

mented in assembly language on the four execution

units in each data path and executed in parallel in or-

der to utilize all the hardware resources of DSP and

exploit all the capabilities of the architecture VLIW

and software pipelining. Because most of the millions

of instructions per second (MIPS) in DSP applications

occur in tight loops, it is important for the application

to make maximal use of all the hardware resources in

important loops. Fortunately, loops have more inher-

ent parallelism than non-looping code because multi-

ple iterations of the same code are executed with lim-

ited dependency between each iteration.

To maximize the efﬁciency of the code, the ap-

plication schedules as many instruction as possible

in parallel. For this, the relationships, or dependen-

cies, between instructions were determined. Figure 1

shows the assembly instructions for ﬂoating-point ac-

tive contour and allocated resources.The symbol || de-

ﬁnes which instruction is being executed in parallel,

while the symbol @ deﬁnes which iteration of the loop

is executing each cycle. For example, the rightmost

column shows that on during given cycle inside the

loop, the ADDSP instruction is adding data for iter-

ation n, the MPYSP instruction is multiplying data

for iteration n+2 (@@), and so on. No value can live

in a register for more than the number of cycles in

the loop. Otherwise, iteration n+1 writes into reg-

ister before iteration n has read that register. The

live-too-long problem means that no loop variable can

live longer than the iteration interval, because a child

would then read the parent value for the next itera-

tion. A simple solution is to break up the lifetime of

a variable by inserting move (MV) instructions in or-

der to break up the path of the variables into smaller

pieces and can live for minimum iteration interval.

One way to accomplish if-then-else in assembly code

is by means of conditional instructions in one of the

ﬁve general-purpose registers. Conditional instruc-

tions can handle both the true and false cases of if-

then-else statement. Branching is one way, although,

because each branch has ﬁve delay slots, this method

requires additional cycles. Furthermore, branching

A DSP-BASED ACTIVE CONTOUR MODEL

427

within the loop makes software pipelining almost im-

possible. Using conditional instructions eliminates

the need to branch to the appropriate piece of code af-

ter checking the condition. For this, instructions can

be used on the zero and nonzero values of a condition

the loop and achieve much better performance.

4 RESULTS AND DISCUSSION

The high speed contour tracking procedure was tested

on sequences of cardiovascular images. The video

was captured with a resolution of 512x512 pixels, 8

bits per pixel, 25 frames per second.

The procedure, designed to track the endocardial

walls, used a hybrid snake to segment the ﬁrst frame

of the ultrasound sequence (see Figure 4). In the

next frames, the active contour is located under the

GVF force derived from a GVF ﬁeld of reduced dif-

fusion. A GVF ﬁeld of reduced diffusion cannot close

all the discontinuities, and so if the pressure force is

found in any place of the image, it may deform the

contour further of endocardial boundaries or invade

other chambers. In this case, it is necessary to de-

activate the force although there is no GVF ﬁeld. In

this way, the GVF snake (very close to its real con-

tour) is only inﬂuenced by the rest of the concerned

forces and deformations are not produced. Our exper-

iments are based on tracking each chamber in differ-

ent echocardiographic sequences. In summary, Fig-

ure 3 shows the echocardiographic sequence for the

left ventricle (three frames are shown), with the ac-

tive contours achieved in the ﬁrst column and drawn

by a specialist in the second column. The similar-

ity is about a 94.5% for the ﬁrst and second frame

and a 94.7% for the third frame. Similar results were

achieved for different frames of the sequence and for

the others chambers.

The software was implemented and conﬁgured to

process instructions in parallel. To be more exact,

loop kernel can be executed in 7 cycles only. Ev-

ery execution packet involves at least two instructions

executing in parallel, although there are packets with

ﬁve instruction in parallel and an execute packet with

seven instruction in parallel. Therefore, if we sup-

pose our snake has 80 snaxels then the energy of a

neighboring point is calculated in each iteration (7

cycles). Each snaxel has 9 neighbours and, therefore,

80×9×7 = 5040 cycles are needed for the active con-

tour in a frame. In a complete sequence, in one sec-

ond, 5040 × 25 = 125.800 cycles are needed and the

DSP load is just 0.1% (125K/160M × 100). Given

these circumstances, the implementation can be con-

sidered very satisfactory for the DSP adopted.

We have presented a high-speed contour tracking

;** --------------------------------------*

L3: ; PIPED LOOP KERNEL

ADDSP .L2 B4,B7,B4 ; |27|

|| MV .S2X A5,B4 ; twin reg.

|| MV .L1 A6,A0 ; @@long l.

|| [ A1] LDW .D2T2 *B5,B6 ; @@|27|

|| [ A1] LDW .D1T1 *A0,A5 ; @@|27|

[ B0] B .S2 L3 ; |29|

|| SUBSP .L1 A10,A5,A4 ; @|27|

|| SUBSP .L2X B8,A5,B4 ; @|27|

|| [ A1] LDW .D1T2 *A3,B5 ; @@|27|

|| MV .S1 A7,A6 ; @@@longl.

ADDSP .L2X A4,B4,B7 ; |27|

|| ADDSP .L1 A7,A11,A5 ; @@@@|27|

[ A1] SUB .D1 A1,1,A1 ;

|| SUBSP .L2X B6,A3,B7 ; @|27|

|| SUBSP .L1 A10,A4,A5 ; @|27|

|| SHL .S1 A5,3,A3 ; @@@|27|

[!B2] STW .D2T2 B4,*++B9(12); |27|

|| MPYSP .M1 A14,A0,A5 ; @@|27|

|| ADDAW .D1 A3,A5,A3 ; @@@|27|

|| ADD .S1 1,A9,A9 ; @@@@|29|

[ B0] SUB .S2 B0,1,B0 ; @|29|

|| MPYSP .M1 A0,A5,A3 ; @@|27|

|| ADD .S1 A2,A3,A3 ; @@@

|| ADD .D1 A12,A3,A13 ; @@@

|| INTSP .L1 A9,A7 ; @@@@@|27|

[ B2] MPYSU .M2 2,B2,B2 ;

|| [!B2] STW .D2T2 B7,*+B9(4) ; |27|

|| MPYSP .M1X A0,B5,A4 ; @@|27|

|| ADD .S2X B1,A13,B5 ; @@@|27|

|| ADD .S1 A8,A13,A0 ; @@@|27|

|| ADD .D1 A8,A3,A3 ; @@@|27|

|| SPTRUNC .L1 A5,A5 ; @@@@|27|

Figure 1: Loop kernel for the active energy algorithm

ICINCO 2004 - ROBOTICS AND AUTOMATION

428

0 359

(a) GVF ﬁeld of reduced

diffusion

(b) Hybrid snake

a expert human

Figure 2: Left ventricle boundary delimitation

Figure 3: Tracking of the left ventricle in a sequence of

ultrasound images.

procedure where a snake is used to contour the endo-

cardium in a sequence of echocadiographic images.

Our preliminary results are encouraging. The main

effort consists in the development of a user interface.

This interface must communicate the host with the ap-

plication running on the DSP, must initialize the active

contour and set up the tuning parameters. Our sys-

tem should be tested with other echographic images

to measure its real performance and tuning capacity.

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